KR102616031B1 - Apparatus and method for implementing physics engine using visual localization - Google Patents

Abstract

A method for visual localization performed by a computing device in accordance with some embodiments of the present disclosure, comprising: receiving a query image obtained from a user device; Obtaining a result of visual localization corresponding to the query image by performing a visual positioning system (VPS) operation based on the query image; determining a collision map corresponding to a result of the visual localization; And when arranging virtual lighting on the collision map, determining at least one of a first virtual light and dark on the collision map or a first visual effect according to the first virtual light and dark based on the information on the virtual light. May include ;.

Description

Method and device for implementing a physics engine using visual localization {APPARATUS AND METHOD FOR IMPLEMENTING PHYSICS ENGINE USING VISUAL LOCALIZATION}

This disclosure relates to image processing and more specifically to visual localization.

As the demand for Location Based Services increases, the need for accurate location information has increased. The most common way to determine location on mobile devices and mobile platforms is GNSS (Global Navigation Satellite System). However, because GNSS signals may be blocked by obstacles in an indoor environment, there is a limitation that they can only be easily used in an outdoor environment.

Although various technologies have been proposed to perform location recognition indoors, the reality is that many indoor location recognition techniques do not go beyond the fingerprint printing-based location recognition algorithm using wireless signals. In this method, the collected data related to Wi-Fi RSS (Received Signal Strength) or MFS (Magnetic Field Strength) is compared with data from a fingerprinting database. Fingerprinting-based systems have the advantage of being easy to build, but it can be difficult to maintain good performance because the signal pattern itself is affected by changes in the system environment. To overcome the deficiencies of these fingerprint-based systems, many alternatives have been proposed, including Optical, RFID (Radio Frequency Identification), Bluetooth Beacons, ZigBee, and Pseudo Satellite, but these alternatives also have difficulty achieving high accuracy in complex indoor environments. It is evaluated as difficult.

Recently, research on VPS (Visual Positioning System) has been actively conducted as an alternative to implement highly accurate position estimation even in indoor environments. VPS technology can also be expressed as visual localization technology, which refers to a technology that estimates the current location or pose of a device using images taken indoors or outdoors. Pose estimation of a device (or camera) refers to determining the translation and rotation information of a dynamically changing camera viewpoint. This camera pose estimation technology is being used in a variety of fields such as mixed reality, augmented reality, robot navigating, and 3-Dimensional Scene Reconstruction.

Augmented reality technology is a technology that enhances work efficiency by augmenting virtual information in real space in real time and allowing users to interact with the augmented virtual information. As augmented reality technology has recently developed, research is being conducted to implement a physical engine in augmented reality for realistic production.

Republic of Korea registered patent 10-2225093

It was developed in response to the above-described background technology of this disclosure, and is intended to implement a precise physics engine using visual localization.

The technical problems of the present disclosure are not limited to the technical problems mentioned above, and other technical problems not mentioned will be clearly understood by those skilled in the art from the description below.

According to some embodiments of the present disclosure for solving the problems described above, a method for visual localization performed by a computing device includes receiving a query image obtained from a user device. ; Obtaining a result of visual localization corresponding to the query image by performing a visual positioning system (VPS) operation based on the query image; determining a collision map corresponding to a result of the visual localization; And when arranging virtual lighting on the collision map, determining at least one of a first virtual light and dark on the collision map or a first visual effect according to the first virtual light and dark based on the information on the virtual light. May include ;.

Alternatively, the results of the visual localization may include a pose that includes an associated location and orientation of the user device.

Alternatively, the collision map may be generated based on information about the space associated with the query image, scanned or photographed using an imaging device.

Alternatively, information representing physical phenomena may be reflected in advance in the collision map.

Alternatively, the information representing the physical phenomenon may include at least one of material information or spatial information of an object included in the collision map.

Alternatively, the material information of the object may include at least one of temperature, viscosity, absorption rate, reflectance, transmittance, scattering rate, attenuation rate, elastic coefficient, or friction coefficient at the volume or surface of the object.

Alternatively, the spatial information may include gravitational field, magnetic field, electric field, Coriolis force, wind direction, wind speed, temperature, humidity, air resistance, altitude above sea level, air density, viscosity, scattering rate, atmospheric pressure, elastic coefficient, friction coefficient, It may include at least one of a vector field or a tensor field.

Alternatively, the information on the virtual lighting may include at least one of location information of the virtual lighting, direction information of the virtual lighting, or characteristic information of the virtual lighting.

Alternatively, the characteristic information of the virtual lighting may include information related to at least one of the color, brightness, reach, or shadow caused by the virtual lighting.

Alternatively, the first visual effect may include an effect corresponding to at least one of overexposure, backlight, dark adaptation, glare, or bokeh.

Alternatively, when placing a virtual object on the collision map, a second virtual brightness or a second visual light according to the second virtual brightness on the collision map based on the information of the virtual lighting and the information of the virtual object It may further include determining at least one of the effects.

Alternatively, the information on the virtual object may include at least one of location information of the virtual object, direction information of the virtual object, or material information of the virtual object.

Alternatively, the material information of the virtual object may include at least one of temperature, viscosity, absorption rate, reflectance, transmittance, scattering rate, attenuation rate, elastic coefficient, or friction coefficient at the volume or surface of the virtual object.

Alternatively, after determining the collision map, comparing the query image and the collision map to obtain information about real-world lighting associated with the user device; determining information on complex lighting based on the information on the virtual lighting and the information on the real lighting; and determining a third virtual brightness on the collision map and a third visual effect according to the third virtual brightness based on the information on the complex lighting.

Alternatively, comparing the query image and the collision map to obtain information about real-world lighting associated with the user device may include comparing the query image and the collision map, determining a difference in light and dark in the image, and using the determined difference in light and dark to estimate information on real lighting on the query image that does not exist in the collision map; and information on the virtual lighting. and determining information on complex lighting based on the information on the real lighting, using the estimated real lighting information, position information on the virtual lighting, direction information on the virtual lighting, and characteristic information on the virtual lighting. It may include estimating information on the complex lighting.

Alternatively, when placing a virtual object on the collision map, a fourth virtual brightness or a fourth visual brightness according to the fourth virtual brightness on the collision map based on the information of the complex lighting and the information of the virtual object It may further include determining at least one of the effects.

Alternatively, when placing a virtual sound on the collision map, the method may further include determining a first auditory effect applied to the user device on the collision map based on information on the virtual sound. there is.

Alternatively, when placing a virtual object on the collision map, determining a second auditory effect applied to the user device on the collision map based on the information of the virtual sound and the information of the virtual object. It may further include ;.

Alternatively, a computer program stored on a computer-readable storage medium, the computer program comprising instructions for causing a processor of a computing device for visual localization to perform the following steps, said steps comprising: Receiving a query image obtained from a device; Obtaining a result of visual localization corresponding to the query image by performing a visual positioning system (VPS) operation based on the query image; determining a collision map corresponding to a result of the visual localization; And when arranging virtual lighting on the collision map, determining at least one of a first virtual light and dark on the collision map or a first visual effect according to the first virtual light and dark based on the information on the virtual light. May include ;.

Alternatively, a computing device for visual localization comprising: a processor including at least one core; a memory storing a computer program executable by the processor; and a network unit, wherein the processor receives a query image obtained from a user device and performs a VPS (visual positioning system) operation based on the query image, thereby generating a query image corresponding to the query image. When obtaining a result of visual localization, determining a collision map corresponding to the result of the visual localization, and placing virtual lighting on the collision map, the collision is performed based on the information of the virtual lighting. At least one of a first virtual brightness on the entire map or a first visual effect according to the first virtual brightness can be determined.

Methods and devices according to some embodiments of the present disclosure can precisely implement a physics engine using visual localization.

The effects that can be obtained from the present disclosure are not limited to the effects mentioned above, and other effects not mentioned can be clearly understood by those skilled in the art from the description below. .

Various aspects will now be described with reference to the drawings, where like reference numerals are used to collectively refer to like elements. In the following examples, for purposes of explanation, numerous specific details are set forth to provide a comprehensive understanding of one or more aspects. However, it will be clear that such aspect(s) may be practiced without these specific details.
1 schematically shows a block diagram of a computing device according to an embodiment of the present disclosure.
Figure 2 is a schematic diagram illustrating a network function according to an embodiment of the present disclosure.
Figure 3 exemplarily shows a method for implementing a physics engine using visual localization according to an embodiment of the present disclosure.
Figure 4 exemplarily shows a method for estimating a pose through visual localization according to an embodiment of the present disclosure.
Figures 5 and 6 exemplarily show a method for implementing a physics engine in a collision map using visual localization according to an embodiment.
Figure 7 depicts a brief, general schematic diagram of an example computing environment in which embodiments of the present disclosure may be implemented.

Various embodiments are now described with reference to the drawings. In this specification, various descriptions are presented to provide an understanding of the disclosure. However, it is clear that these embodiments may be practiced without these specific descriptions.

As used herein, the terms “component”, “component”, “module”, “system”, etc. refer to a computer-related entity, hardware, firmware, software, a combination of software and hardware, or an implementation of software. For example, a component may be, but is not limited to, a process running on a processor, a processor, an object, a thread of execution, a program, and/or a computer. For example, both an application running on a computing device and the computing device can be a component. One or more components may reside within a processor and/or thread of execution. A component may be localized within one computer. A component may be distributed between two or more computers. Additionally, these components can execute from various computer-readable media having various data structures stored thereon. Components can transmit signals, for example, with one or more data packets (e.g., data and/or signals from one component interacting with other components in a local system, a distributed system, to other systems and over a network such as the Internet). Depending on the data being transmitted, they may communicate through local and/or remote processes.

Additionally, the term “or” is intended to mean an inclusive “or” and not an exclusive “or.” That is, unless otherwise specified or clear from context, “X utilizes A or B” is intended to mean one of the natural implicit substitutions. That is, either X uses A; X uses B; Or, if X uses both A and B, “X uses A or B” can apply to either of these cases. Additionally, the term “and/or” as used herein should be understood to refer to and include all possible combinations of one or more of the related listed items.

Additionally, the terms “comprise” and/or “comprising” should be understood to mean that the corresponding feature and/or element is present. However, the terms “comprise” and/or “comprising” should be understood as not excluding the presence or addition of one or more other features, elements and/or groups thereof. Additionally, unless otherwise specified or the context is clear to indicate a singular form, the singular terms herein and in the claims should generally be construed to mean “one or more.”

And, the term “at least one of A or B” should be interpreted to mean “if it contains only A,” “if it contains only B,” or “if it is a combination of A and B.”

Those skilled in the art will additionally recognize that the various illustrative logical blocks, components, modules, circuits, means, logic, and algorithm steps described in connection with the embodiments disclosed herein may be implemented using electronic hardware, computer software, or a combination of both. It must be recognized that it can be implemented with To clearly illustrate the interchangeability of hardware and software, various illustrative components, blocks, configurations, means, logics, modules, circuits, and steps have been described above generally in terms of their functionality. Whether such functionality is implemented in hardware or software will depend on the specific application and design constraints imposed on the overall system. A skilled technician can implement the described functionality in a variety of ways for each specific application. However, such implementation decisions should not be construed as causing a departure from the scope of the present disclosure.

The description of the presented embodiments is provided to enable anyone skilled in the art to use or practice the present invention. Various modifications to these embodiments will be apparent to those skilled in the art. The general principles defined herein may be applied to other embodiments without departing from the scope of the disclosure. Therefore, the present invention is not limited to the embodiments presented herein. The present invention is to be interpreted in the broadest scope consistent with the principles and novel features presented herein.

The description of the presented embodiments is provided to enable anyone skilled in the art to use or practice the present invention. Various modifications to these embodiments will be apparent to those skilled in the art. The general principles defined herein may be applied to other embodiments without departing from the scope of the disclosure. Therefore, the present invention is not limited to the embodiments presented herein. The present invention is to be interpreted in the broadest scope consistent with the principles and novel features presented herein.

In the present disclosure, terms represented by N, such as first, second, or third, are used to distinguish a plurality of entities. For example, the entities expressed as first and second may be the same or different from each other.

In the present disclosure, visual localization may refer to a technology for estimating a pose including the current location or direction of a device using images captured indoors or outdoors. Pose estimation of a device (eg, camera) may mean determining translation and rotation information of a dynamically changing camera viewpoint.

The pose in the present disclosure may refer to result information obtained through visual localization, including, for example, the position and direction of a user device (eg, a device including a camera).

As another example, a pose in the present disclosure may include state information corresponding to, for example, the user's appearance and/or movements. In this example, the pose may be obtained by a deep learning module that includes an algorithm for estimating the user's pose. In this example, the pose may be obtained based on visual localization and/or inverse kinematics.

1 schematically shows a block diagram of a computing device 100 according to an embodiment of the present disclosure.

The computing device 100 according to an embodiment of the present disclosure may include a processor 110, a memory 130, and a network unit 150.

The configuration of the computing device 100 shown in FIG. 1 is only a simplified example. In one embodiment of the present disclosure, the computing device 100 may include different configurations for performing the computing environment of the computing device 100, and only some of the disclosed configurations may configure the computing device 100.

The computing device 100 in the present disclosure may refer to any type of node constituting a system for implementing embodiments of the present disclosure. Computing device 100 may refer to any type of user device or any type of server. The components of the computing device 100 described above are exemplary and some may be excluded or additional components may be included. For example, when the above-described computing device 100 includes a user device, an output unit (not shown) and an input unit (not shown) may be included within the scope of the computing device 100.

In one embodiment, the processor 110 may consist of at least one core, including a central processing unit (CPU) and a general purpose graphics processing unit (GPGPU) of the computing device 100. , may include a processor for data analysis and/or processing, such as a tensor processing unit (TPU).

In one embodiment, the processor 110 receives a query image obtained from a user device to perform visual localization, and performs a visual positioning system (VPS) operation based on the query image to determine the query image. When obtaining the result of visual localization corresponding to the result, determining a collision map corresponding to the result of the visual localization, and placing virtual lighting on the collision map, collision is performed based on the information of the virtual lighting. At least one of the first virtual light and dark on the map or the first visual effect according to the first virtual light and dark can be determined.

The processor 110 may read a computer program stored in the memory 130 and perform visual localization methodologies according to an embodiment of the present disclosure.

According to an embodiment of the present disclosure, the processor 110 may perform an operation for learning a neural network. The processor 110 is used for learning neural networks, such as processing input data for learning in deep learning (DL), extracting features from input data, calculating errors, and updating the weights of the neural network using backpropagation. Calculations can be performed. At least one of the CPU, GPGPU, and TPU of the processor 110 may process learning of the network function. For example, CPU and GPGPU can work together to process learning of network functions and data classification using network functions. Additionally, in one embodiment of the present disclosure, processors of a plurality of computing devices may be used together to process learning of a network function and data classification using a network function. Additionally, a computer program executed in a computing device according to an embodiment of the present disclosure may be a CPU, GPGPU, or TPU executable program. Operations for a neural network according to an embodiment of the present disclosure will be described later with reference to FIG. 2.

Additionally, processor 110 may typically handle overall operations of computing device 100. For example, the processor 110 processes data, information, or signals input or output through components included in the computing device 100 or runs an application program stored in the memory 130 to provide appropriate information to the user. Information or functions may be provided or processed.

According to one embodiment of the present disclosure, the memory 130 may store any type of information generated or determined by the processor 110 and any type of information received by the computing device 100. According to an embodiment of the present disclosure, the memory 130 may be a storage medium that stores computer software that allows the processor 110 to perform operations according to embodiments of the present disclosure. Accordingly, the memory 130 may refer to computer readable media for storing software code required to perform embodiments of the present disclosure, data to be executed by the code, and execution results of the code.

According to one embodiment of the present disclosure, the memory 130 may refer to any type of storage medium. For example, the memory 130 may be a flash memory type or a hard disk type. ), multimedia card micro type, card type memory (e.g. SD or XD memory, etc.), RAM (Random Access Memory), SRAM (Static Random Access Memory), ROM (Read-Only) Memory, ROM), EEPROM (Electrically Erasable Programmable Read-Only Memory), PROM (Programmable Read-Only Memory), magnetic memory, magnetic disk, and optical disk. The computing device 100 may operate in connection with web storage that performs a storage function of the memory 130 on the Internet. The description of the memory described above is only an example, and the memory 130 used in the present disclosure is not limited to the examples described above.

In one embodiment, the network unit 150 may be configured regardless of communication mode, such as wired or wireless, and may be configured in various communication networks such as a personal area network (PAN) and a wide area network (WAN). It can be composed of . In addition, the network unit 150 can operate based on the World Wide Web (WWW) and can use wireless transmission technology used for short-distance communication, such as Infrared Data Association (IrDA) or Bluetooth. It may be possible.

Computing device 100 in this disclosure may include any type of user device and/or any type of server. Accordingly, embodiments of the present disclosure may be performed by a server and/or a user device.

A user device may include any type of device capable of interacting with a server or other computing device. User devices include, for example, mobile phones, smart phones, laptop computers, personal digital assistants (PDAs), slate PCs, tablet PCs, and ultrabooks. It can be included.

In one embodiment of the present disclosure, a user device may refer to any type of equipment for detecting an optical image, converting it into an electrical signal, and transmitting it to the computing device 100. For example, the user device may include at least one of a camera, scanner, Lidar, and/or vision sensor. In additional embodiments, the computing device 100 may include a user device or may be linked to an external device wirelessly or wired.

User devices in this disclosure may include any type of mobile device, including cameras and/or lidar. For example, the user device may include a head mounted device (HMD), augmented reality (AR) glasses, and/or virtual reality (VR) glasses.

Hereinafter, for convenience of explanation, the user device and the computing device 100 will be distinguished, and a methodology for visual localization for the computing device 100 to receive shooting information from the user device and estimate the pose of the user device will be described. It will be explained. However, embodiments of performing visual localization on a user device that generates query data (eg, query image, etc.) may also be included within the scope of the present disclosure. In this embodiment, the user device may perform at least part of the role of the computing device 100.

Servers may include any type of computing system or computing device, such as, for example, microprocessors, mainframe computers, digital processors, portable devices, and device controllers. In one embodiment, the server is a server for performing a Visual Positioning System (VPS) and may store a collision map. The server may perform pose estimation for the user device, determination of a collision map for the estimated pose, and determination of contrast and visual effects in the collision map based on the VPS using an image acquired from the user device.

In an additional embodiment, the above-described server may include a storage unit (not shown) that stores and manages query images, collision maps, etc. This storage may be included within the server or may exist under the management of the server. As another example, the storage unit may be implemented in a form that exists outside the server and can communicate with the server. In this case, the storage may be managed and controlled by an external server that is different from the server.

Figure 2 is a schematic diagram illustrating a network function according to an embodiment of the present disclosure.

Throughout this disclosure, artificial intelligence-based model (e.g., feature point acquisition model, etc.), segmentation model, computational model, neural network, network function, and neural network may be used with the same meaning. A neural network can generally consist of a set of interconnected computational units, which can be referred to as nodes. These nodes may also be referred to as neurons. A neural network consists of at least one node. Nodes (or neurons) that make up neural networks may be interconnected by one or more links.

Within a neural network, one or more nodes connected through a link may form a relative input node and output node relationship. The concepts of input node and output node are relative, and any node in an output node relationship with one node may be in an input node relationship with another node, and vice versa. As described above, input node to output node relationships can be created around links. One or more output nodes can be connected to one input node through a link, and vice versa.

In a relationship between an input node and an output node connected through one link, the value of the data of the output node may be determined based on the data input to the input node. Here, the link connecting the input node and the output node may have a weight. Weights may be variable and may be varied by the user or algorithm in order for the neural network to perform the desired function. For example, when one or more input nodes are connected to one output node by respective links, the output node is set to the values input to the input nodes connected to the output node and the links corresponding to each input node. The output node value can be determined based on the weight.

As described above, in a neural network, one or more nodes are interconnected through one or more links to form an input node and output node relationship within the neural network. The characteristics of the neural network can be determined according to the number of nodes and links within the neural network, the correlation between the nodes and links, and the value of the weight assigned to each link. For example, if the same number of nodes and links exist and two neural networks with different weight values of the links exist, the two neural networks may be recognized as different from each other.

A neural network may consist of a set of one or more nodes. A subset of nodes that make up a neural network can form a layer. Some of the nodes constituting the neural network may form one layer based on the distances from the first input node. For example, a set of nodes with a distance n from the initial input node may constitute n layers. The distance from the initial input node can be defined by the minimum number of links that must be passed to reach the node from the initial input node. However, this definition of a layer is arbitrary for explanation purposes, and the order of a layer within a neural network may be defined in a different way than described above. For example, a layer of nodes may be defined by distance from the final output node.

The initial input node may refer to one or more nodes in the neural network through which data is directly input without going through links in relationships with other nodes. Alternatively, in a neural network network, in the relationship between nodes based on links, it may mean nodes that do not have other input nodes connected by links. Similarly, the final output node may refer to one or more nodes that do not have an output node in their relationship with other nodes among the nodes in the neural network. Additionally, hidden nodes may refer to nodes constituting a neural network other than the first input node and the last output node.

The neural network according to an embodiment of the present disclosure is a neural network in which the number of nodes in the input layer may be the same as the number of nodes in the output layer, and the number of nodes decreases and then increases again as it progresses from the input layer to the hidden layer. You can. In addition, the neural network according to another embodiment of the present disclosure may be a neural network in which the number of nodes in the input layer may be less than the number of nodes in the output layer, and the number of nodes decreases as it progresses from the input layer to the hidden layer. there is. In addition, the neural network according to another embodiment of the present disclosure may be a neural network in which the number of nodes in the input layer may be greater than the number of nodes in the output layer, and the number of nodes increases as it progresses from the input layer to the hidden layer. You can. A neural network according to another embodiment of the present disclosure may be a neural network that is a combination of the above-described neural networks.

A deep neural network (DNN) may refer to a neural network that includes multiple hidden layers in addition to the input layer and output layer. Deep neural networks allow you to identify latent structures in data. In other words, it is possible to identify the potential structure of a photo, text, video, voice, or music (e.g., what object is in the photo, what the content and emotion of the text are, what the content and emotion of the voice are, etc.) . Deep neural networks include convolutional neural network (CNN), recurrent neural network (RNN), auto encoder, restricted Boltzmann machine (RBM), and deep trust network ( It may include deep belief network (DBN), Q network, U network, Siamese network, generative adversarial network (GAN), etc. The description of the deep neural network described above is only an example and the present disclosure is not limited thereto.

A neural network may be trained in at least one of supervised learning, unsupervised learning, semi-supervised learning, or reinforcement learning. Learning of a neural network may be a process of applying knowledge for the neural network to perform a specific operation to the neural network.

Neural networks can be trained to minimize output errors. In neural network learning, learning data is repeatedly input into the neural network, the output of the neural network and the error of the target for the learning data are calculated, and the error of the neural network is transferred from the output layer of the neural network to the input layer in the direction of reducing the error. This is the process of updating the weight of each node in the neural network through backpropagation. In the case of supervised learning, learning data in which the correct answer is labeled for each learning data is used (i.e., labeled learning data), while in the case of unsupervised learning, the correct answer may not be labeled in each learning data. That is, for example, in the case of supervised learning on data classification, the training data may be data in which each training data is labeled with a category. Labeled training data is input to the neural network, and the error can be calculated by comparing the output (category) of the neural network with the label of the training data. As another example, in the case of unsupervised learning on data classification, the error can be calculated by comparing the input training data with the neural network output. The calculated error is backpropagated in the reverse direction (i.e., from the output layer to the input layer) in the neural network, and the connection weight of each node in each layer of the neural network can be updated according to backpropagation. The amount of change in the connection weight of each updated node may be determined according to the learning rate. The neural network's calculation of input data and backpropagation of errors can constitute a learning cycle (epoch). The learning rate may be applied differently depending on the number of repetitions of the learning cycle of the neural network. For example, in the early stages of neural network training, a high learning rate can be used to increase efficiency by allowing the neural network to quickly achieve a certain level of performance, and in the later stages of training, a low learning rate can be used to increase accuracy.

In the learning of neural networks, the training data can generally be a subset of real data (i.e., the data to be processed using the learned neural network), and thus the error for the training data is reduced, but the error for the real data is reduced. There may be an incremental learning cycle. Overfitting is a phenomenon in which errors in actual data increase due to excessive learning on training data. For example, a phenomenon in which a neural network that learned a cat by showing a yellow cat fails to recognize that it is a cat when it sees a non-yellow cat may be a type of overfitting. Overfitting can cause errors in machine learning algorithms to increase. To prevent such overfitting, various optimization methods can be used. To prevent overfitting, methods such as increasing the learning data, regularization, dropout to disable some of the network nodes during the learning process, and use of a batch normalization layer can be applied. You can.

A computer-readable medium storing a data structure according to an embodiment of the present disclosure is disclosed.

Data structure can refer to the organization, management, and storage of data to enable efficient access and modification of data. Data structure can refer to the organization of data to solve a specific problem (e.g., retrieving data, storing data, or modifying data in the shortest possible time). A data structure may be defined as a physical or logical relationship between data elements designed to support a specific data processing function. Logical relationships between data elements may include connection relationships between user-defined data elements. Physical relationships between data elements may include actual relationships between data elements that are physically stored in a computer-readable storage medium (e.g., a persistent storage device). A data structure may specifically include a set of data, relationships between data, and functions or instructions applicable to the data. Effectively designed data structures allow computing devices to perform computations while minimizing the use of the computing device's resources. Specifically, computing devices can increase the efficiency of operations, reading, insertion, deletion, comparison, exchange, and search through effectively designed data structures.

Data structures can be divided into linear data structures and non-linear data structures depending on the type of data structure. A linear data structure may be a structure in which only one piece of data is connected to another piece of data. Linear data structures may include List, Stack, Queue, and Deque. A list can refer to a set of data that has an internal order. The list may include a linked list. A linked list may be a data structure in which data is connected in such a way that each data is connected in a single line with a pointer. In a linked list, a pointer may contain connection information to the next or previous data. Depending on its form, a linked list can be expressed as a singly linked list, a doubly linked list, or a circularly linked list. A stack may be a data listing structure that allows limited access to data. A stack can be a linear data structure in which data can be processed (for example, inserted or deleted) at only one end of the data structure. Data stored in the stack may have a data structure (LIFO-Last in First Out) where the later it enters, the sooner it comes out. A queue is a data listing structure that allows limited access to data. Unlike the stack, it can be a data structure (FIFO-First in First Out) where data stored later is released later. A deck can be a data structure that can process data at both ends of the data structure.

A non-linear data structure may be a structure in which multiple pieces of data are connected behind one piece of data. Nonlinear data structures may include graph data structures. A graph data structure can be defined by vertices and edges, and an edge can include a line connecting two different vertices. Graph data structure may include a tree data structure. A tree data structure may be a data structure in which there is only one path connecting two different vertices among a plurality of vertices included in the tree. In other words, it may be a data structure that does not form a loop in the graph data structure.

Throughout this specification, artificial intelligence-based model, computational model, neural network, network function, and neural network may be used with the same meaning. Below, it is described in a unified manner as a neural network. Data structures may include neural networks. And the data structure including the neural network may be stored in a computer-readable medium. Data structures including neural networks also include data preprocessed for processing by a neural network, data input to the neural network, weights of the neural network, hyperparameters of the neural network, data acquired from the neural network, activation functions associated with each node or layer of the neural network, neural network It may include a loss function for learning. A data structure containing a neural network may include any of the components disclosed above. In other words, the data structure including the neural network includes preprocessed data for processing by the neural network, data input to the neural network, weights of the neural network, hyperparameters of the neural network, data acquired from the neural network, activation functions associated with each node or layer of the neural network, neural network It may be configured to include all or any combination of the loss function for learning. In addition to the configurations described above, a data structure containing a neural network may include any other information that determines the characteristics of the neural network. Additionally, the data structure may include all types of data used or generated in the computational process of a neural network and is not limited to the above. Computer-readable media may include computer-readable recording media and/or computer-readable transmission media. A neural network can generally consist of a set of interconnected computational units, which can be referred to as nodes. These nodes may also be referred to as neurons. A neural network consists of at least one node.

The data structure may include data input to the neural network. A data structure containing data input to a neural network may be stored in a computer-readable medium. Data input to the neural network may include learning data input during the neural network learning process and/or input data input to the neural network on which training has been completed. Data input to the neural network may include data that has undergone pre-processing and/or data subject to pre-processing. Preprocessing may include a data processing process to input data into a neural network. Therefore, the data structure may include data subject to preprocessing and data generated by preprocessing. The above-described data structure is only an example and the present disclosure is not limited thereto.

The data structure may include the weights of the neural network. In this disclosure, weights and parameters may be used interchangeably. The data structure including the weights of the neural network may be stored in a computer-readable medium. A neural network may include multiple weights. Weights may be variable and may be varied by the user or algorithm in order for the neural network to perform the desired function. For example, when one or more input nodes are connected to one output node by respective links, the output node is set to the values input to the input nodes connected to the output node and the links corresponding to each input node. Based on the weight, the data value output from the output node can be determined. The above-described data structure is only an example and the present disclosure is not limited thereto.

As an example and not a limitation, the weights may include weights that are changed during the neural network learning process and/or weights for which neural network learning has been completed. Weights that change during the neural network learning process may include weights that change at the start of the learning cycle and/or weights that change during the learning cycle. Weights for which neural network training has been completed may include weights for which a learning cycle has been completed. Therefore, a data structure including weights of a neural network may include weights that change during the neural network learning process and/or weights that have completed neural network learning. Therefore, the above-described weights and/or combinations of each weight are included in the data structure including the weights of the neural network. The above-described data structure is only an example and the present disclosure is not limited thereto.

The data structure including the weights of the neural network may be stored in a computer-readable storage medium (e.g., memory, hard disk) after going through a serialization process. Serialization can be the process of converting a data structure into a form that can be stored on the same or a different computing device and later reorganized and used. Computing devices can transmit and receive data over a network by serializing data structures. Data structures containing the weights of a serialized neural network can be reconstructed on the same computing device or on a different computing device through deserialization. The data structure including the weights of the neural network is not limited to serialization. Furthermore, the data structure including the weights of the neural network is a data structure to increase computational efficiency while minimizing the use of computing device resources (e.g., in non-linear data structures, B-Tree, Trie, m-way search tree, AVL tree, Red-Black Tree) may be included. The foregoing is merely an example and the present disclosure is not limited thereto.

The data structure may include hyper-parameters of a neural network. And the data structure including the hyperparameters of the neural network can be stored in a computer-readable medium. A hyperparameter may be a variable that can be changed by the user. Hyperparameters include, for example, learning rate, cost function, number of learning cycle repetitions, weight initialization (e.g., setting the range of weight values subject to weight initialization), Hidden Unit. It may include a number (e.g., number of hidden layers, number of nodes in hidden layers). The above-described data structure is only an example and the present disclosure is not limited thereto.

Figure 3 exemplarily shows a method for implementing a physics engine using visual localization according to an embodiment of the present disclosure. The steps shown in FIG. 3 are exemplary steps. Accordingly, it will also be apparent to those skilled in the art that some of the steps in FIG. 3 may be omitted or additional steps may be present without departing from the scope of the present disclosure.

The flowchart shown in FIG. 3 may be performed, for example, by computing device 100.

In some embodiments of the present disclosure, computing device 100 may provide an augmented reality and/or virtual reality platform. The computing device 100 may estimate the current location and direction of the user device by acquiring data (eg, query image, etc.) captured from a user device equipped with a photographing function. Computing device 100 may allow a client to enjoy activities on an augmented reality and/or virtual reality platform by estimating the location and orientation of the user device.

In one embodiment, computing device 100 may receive a query image obtained from a user device (310).

In one embodiment, images acquired by the user device may include two-dimensional RGB images or grayscale images.

In one embodiment, images acquired by a user device may include a plurality of images acquired over time with respect to the target space. For example, the images may include successive images of at least a portion of the target space. The target space may mean an indoor or outdoor space of any shape and size in which visual localization can be performed.

In additional embodiments, computing device 100 may obtain additional information of the user device (eg, camera information of the user device). For example, additional information about the user device may be obtained along with acquiring the images. Additional information may include location information of the user device. Location information is, for example, geodetic datum or geodetic system (or geodetic reference datum, geodetic reference system, or geodetic reference frame), spatial reference system (SRS) or coordinate reference system (CRS) used in a specific region or country. , or may include coordinate values and/or direction values for a geographic coordinate system (GCS), etc. Other examples of location information may include GPS.

Additionally, the additional information on the user device may further include hardware-related information on the user device, such as focal length, principal point, resolution, etc. In addition, if there is a record of estimating the past camera pose of the user device, the additional information of the user device includes preliminary estimation information about the camera pose of the user device calculated from the value of the IMU (Inertial Measurement Unit) of the user device. It can be included. This preliminary estimate information about the camera pose of the user device may include an approximate camera pose estimated using IMU information, and this preliminary estimate information may include approximate location information and direction information about the user device. . Because the candidate group of reference images (or reference point clouds) to be matched with the captured image (or point cloud corresponding to the captured image) can be reduced, comparison operations on the server (or computing device) are performed efficiently. It can be. Accordingly, resource-efficient visual localization can be achieved.

The computing device 100 can obtain a result of visual localization (e.g., result information obtained through visual localization, etc.) corresponding to the query image by performing a visual positioning system (VPS) operation based on the query image. There is (320).

The results of visual localization may include a pose that includes the relative location and orientation of the user device. A specific method of obtaining the results of visual localization will be described later with reference to FIG. 4.

The computing device 100 may determine a collision map corresponding to the result of visual localization (330).

The collision map may be generated based on information about the space related to the query image, which is scanned or photographed using an imaging device (eg, device, etc.). The collision map may include a 3D model.

Specifically, the processor 110 may generate a collision map including a 3D model. For example, the processor 110 may provide information about any space (e.g., a space associated with an image and/or depth map received from the device) scanned or photographed using an imaging device (e.g., a device, etc.). Spatial information can be obtained.

An imaging device may refer to any type of equipment for detecting optical images, converting them into electrical signals, and inputting them into the computing device 100. For example, the imaging device may include at least one of a scanner, Lidar, and/or a vision sensor. The computing device 100 may include an imaging device or may be linked to an external imaging device wirelessly or wired.

Spatial information may refer to information about the interior of a specific space. For example, spatial information may mean information about any type of object existing in any space. For example, spatial information may include at least one of distance, direction, and/or speed from an imaging device to an object existing in a certain space. Additionally, the spatial information may include at least one of the characteristics of the object's color, temperature, material distribution, and/or concentration. An object may refer to a software module that includes state data representing a state and operation (eg, procedure, method, function, etc.) data related to the state data. For example, the object may include at least one of a basic object, a moving object, and/or a specific predetermined object.

The processor 110 may generate point cloud information including color data and/or location data based on spatial information.

Point cloud information may refer to information representing a set of points in a three-dimensional space (eg, 3D model, etc.). Point cloud information may refer to location information of measurement points related to the color and/or location of a basic object existing in an arbitrary space. The basic object may be an object excluding a moving object and a predetermined specific object. Accordingly, the point cloud information may be information from which information related to at least one of lighting, a moving object, and/or a predetermined specific object has been removed. Lighting information may be information related to light. A mobile object may be an object that is not permanently fixed but can move at a specific point in time or in a specific situation. For example, a moving object may be an object that can move (eg, a car, a bicycle, etc.). The specific predetermined object may be a floating object (eg, a plant) rather than a moving object. According to one embodiment, the predetermined specific object may be an object set by the user.

Additionally, point cloud information may include color data and/or location data. Color data may include data related to the color of an object existing in an arbitrary space. For example, color data may include vertex color values that include RGB color values of objects existing in arbitrary space. However, color data is not limited to this and may include various values representing colors.

Location data may include data related to the location of an object existing in arbitrary space. For example, location data may include the x, y, and z location coordinates of an object existing in arbitrary space. However, the meaning of location data is not limited to the above-described example, and may include various values indicating the location or depth of an object.

The processor 110 may generate a collision map including a 3D model based on point cloud information.

A 3D model is a model (e.g., a 3D map, etc.) created in three dimensions to correspond to an arbitrary space based on point cloud information, and may include point cloud information. For example, the processor 110 may change the point cloud included in the point cloud information in mesh units to generate a 3D model formed in three dimensions corresponding to an arbitrary space. Accordingly, the processor 110 may generate a 3D model by changing the point cloud included in the point cloud information into various other units. For another example, the processor 110 may generate a 3D model formed from a point cloud included in point cloud information. Accordingly, the processor 110 can generate a 3D model by using the point cloud included in the point cloud information without changing it. Here, the arbitrary space may be a space scanned or photographed using an imaging device, and may be a space related to an image and/or a depth map received from the device.

Meanwhile, in some embodiments of the present disclosure, information representing physical phenomena may be reflected in the collision map in advance.

Information representing a physical phenomenon may include at least one of material information or spatial information of an object included in the collision map.

The material information of an object may include information about the physical elements of the object. For example, the material information of the object may include at least one of temperature, viscosity, absorption rate, reflectance, transmittance, scattering rate, attenuation rate, elastic coefficient, and/or friction coefficient at the volume or surface of the object. The volume of an object may be a three-dimensional area excluding the surface of the object. For example, if the object is in the form of a gas, the volume of the object may refer to the area occupied by the gas. The computing device 100 may set the total sum of the absorption rate, transmittance, and reflectance on the surface of the object to be 1.

Spatial information may include information about physical elements of space. For example, spatial information includes gravitational field, magnetic field, electric field, Coriolis force, wind direction, wind speed, temperature, humidity, air resistance, altitude above sea level, air density, viscosity, scattering rate, atmospheric pressure, elastic coefficient, friction coefficient, and vector field in space. It may include at least one of a vector field or a tensor field.

A vector field may be a set of vector values assigned to each point in space. A vector value may refer to a physical quantity with size and direction, such as position, speed, force, etc. For example, the vector value may include the strength of the gravitational field, the strength of the magnetic field, etc. However, the above-mentioned vector values are only examples and may include various values.

A tensor field may be a set of tensor values assigned to each point in space. The tensor value may be a value representing the moment of inertia or deformation of an object.

The computing device 100 may set temperature, air density, and atmospheric pressure among spatial information to be proportional to the propagation speed of sound and/or the distance of propagation.

Spatial information may include information about non-physical elements of space. For example, spatial information may include at least one of information about whether there is blocking in space, blocking channel information, information about whether there is a trigger, information about a trigger channel, and/or tag information for programming.

The computing device 100 may change material information of an object reflected on a collision map (eg, virtual reality space, augmented reality space, virtual space, etc.) based on spatial information. For example, the computing device 100 may change the evaporation rate, which is one of the material information of the water in the water cup reflected on the collision map, according to the temperature included in the spatial information.

The material information and spatial information of the object reflected on the collision map may change over time based on rules preset on the collision map.

Preset laws may include physical laws, mechanical laws, etc. For example, preset laws may include Newton's law of gravity, Einstein's general theory of relativity, etc. For example, the preset law is the first law in which the strength of attraction is inversely proportional to the cube of the distance, the second law in which the gravitational constant G is greater than the actual value, etc., are arbitrary laws that do not exist previously set by the environment or the user. may include. However, the preset rule is not limited to this and may be determined depending on the environment.

Newton's law of gravity and Einstein's general theory of relativity explain how objects with mass interact with objects with different masses, but even if the assumptions are the same, the results may differ depending on the theory. Therefore, the material information and spatial information of the object reflected on the collision map affect each other (for example, the effect between the material information of the first object and the material information of the second object, the material information of the first object and the first object) (effect between spatial information, influence between first spatial information and second spatial information of a second space different from the first space, etc.), may change over time based on a rule preset on the collision map.

When placing a virtual object on a collision map, the computing device 100 may determine material information of the virtual object in a preset manner. The predetermined method may include a method of arbitrarily determining among a plurality of material information, a method of setting the material information of a real object corresponding to a virtual object, etc.

In some embodiments of the present disclosure, the collision map may reflect object recognition information in advance. Object recognition information is information required to recognize an object and may include at least one of the type of object and/or the location of the object. The memory 130 of the computing device 100 may store at least one of object material information, spatial information, and/or object recognition information in advance.

In some embodiments of the present disclosure, the computing device 100 may map information about an object to an area corresponding to recognition information of the object on a collision map. For example, the computing device 100 may map information about the tree to an area corresponding to the tree on the collision map.

In some embodiments of the present disclosure, the computing device 100 classifies objects included in query data (e.g., query image, etc.) using a pre-trained artificial intelligence-based image classification model or image segmentation model, and Attribute information about the object including material information and object recognition information of a pre-stored object corresponding to the classified object can be obtained. The computing device 100 may automatically reflect the material information about the object on the collision map without manually mapping the material information about the object using this artificial intelligence-based classification or segmentation model.

In some embodiments of the present disclosure, the processor 110 of computing device 100 may generate an environment of a collision map based on state information. The processor 110 may reflect the environment of the generated collision map into the collision map.

State information may be information about at least one parameter related to the environment within the collision map. State information may include at least one of camera information, moving object information, date information, lighting information, weather information, predetermined specific object information, and/or interaction information.

Camera information may be information about a camera used to photograph a specific area in a collision map. For example, camera information may include at least one of camera location information and/or direction information. The location information of the camera may include coordinates where the camera exists. The direction information of the camera may include at least one of the direction and/or angle the camera is looking at. Additionally, the camera information may include at least one of focal length information with a specific area, principal point information, lens information, and/or 'sensor and/or film information.' Focal distance information may be information about the distance from the main point of the camera existing in the collision map to a specific area to be photographed. Principal point information may be information about a point where a principal plane intersects an optical axis. Lens information may be information related to the distortion of the lens applied to the camera. Film information may be information related to the size of the film applied to the camera. Sensor information may be information related to a camera sensor applied to a camera. The processor 110 may determine the aspect ratio (ratio of width to height) of an image acquired through a camera based on sensor or film information. Accordingly, the processor 110 may determine camera settings (eg, camera specifications, etc.) applied to the environment of the collision map based on the camera information included in the state information. However, the camera information is not limited to this and may include information from various cameras.

The moving object information may be information about at least one moving object placed in the collision map. In some embodiments of the present disclosure, at least one moving object may be included in a virtual object. For example, the moving object information may include at least one of type information, movement information, and/or property information of the moving object to be placed in the collision map. The type information of the moving object may include information about at least one of the shape, size, and/or category of the moving object. The movement information of the moving object may be information related to the location of the moving object. For example, the movement information of the moving object may include information about at least one of the initial position, speed, acceleration, and/or path of the moving object. The property information of the moving object may include information about the physical properties of the moving object (eg, material information, etc.). For example, information on the properties of a moving object may include information about collision of the moving object. Accordingly, the processor 110 may determine the settings of the moving object (eg, specifications, location, etc. of the moving object) applied to the environment of the collision map based on the moving object information included in the state information. However, the moving object information is not limited to this and may include information about various moving objects.

Date information may include season information and/or time information to be applied to the collision map. The season information may be information about the standard point of the climate that divides the solar year into 24 parts according to the solar environment. For example, seasonal information may include Ipchun, Usu, Gyeongchip, etc. Time information may be information indicating a point in the day. For example, time information may include 1:01, 13:20, etc.

Weather information may be information related to weather conditions to be applied to the collision map. For example, the weather information may include cloud information regarding at least one of the amount, distribution, height, shape, color, solar transmittance, movement speed, dispersion, and/or shape of clouds. As another example, the weather information may include fog information determined based on at least one of fog intensity, color, scattering intensity, and/or height. The fog information may include at least one of the amount, color, intensity, blur effect, change, and/or self-luminescence of the fog. As another example, the weather information may include at least one of the following: particle amount of snow or/and rain, particle shape, particle color, particle size, particle transparency, particle refractive index, particle speed, and/or wind tilt angle. It can be included.

Lighting information may be information related to light applied to the collision map. Lighting information includes at least one of virtual lighting information (e.g., artificial lighting information, etc.) and/or real lighting information (e.g., natural lighting (e.g., sunlight, etc.) information, etc.) can do. For example, lighting information is one of natural lighting information and may include lighting color information and/or shadow information determined based on date information and weather information. Lighting information is one of natural lighting information and includes information about at least one of the color, intensity, direction, atmospheric scattering, and/or atmospheric reflection of directional light that changes based on date information and weather information. can do. Directional light can be a ray that projects the same amount infinitely in one direction. For example, a directional light could include the sun.

Additionally, the lighting information may include information about at least one of global illumination, color intensity of skylight, direction, atmospheric scattering, and/or atmospheric reflection. Global illumination may be an algorithm that determines the color of objects by considering not only one object and lighting, but also the influence of other objects. Skylight can refer to diffused light that is scattered in the atmosphere from direct sunlight.

Additionally, the lighting information is one of virtual lighting information and may include information about at least one of the color, intensity, direction, atmospheric scattering, and/or atmospheric reflection of the artificial light source. Virtual lighting is a light source that generates light using electricity and may include a point light source, a surface light source, or a directional light source.

Additionally, lighting information may include location and/or direction information of lighting. The location and/or direction information of the lighting may be determined based on the unique geographic location information and date information of the predetermined collision map assigned to the collision map. The location information of the light may include location coordinates where the light exists. Light direction information may include at least one of the direction and/or angle from which the light is viewed.

Shadow information may be information about a shadow determined based on lighting information. Specifically, shadow information of lighting information may be determined based on at least one of location and/or direction information of lighting. For example, the shadow information may include at least one of shadow intensity, shadow length, and/or shadow sharpness for at least one object present in the collision map.

Predetermined specific object information may include information about objects other than moving objects within a specific area. For example, the predetermined specific object information may include information about a floating object (eg, a plant) rather than a moving object. For example, the predetermined specific object information may include type information and property information of the predetermined specific object to be placed in the collision map. The type of a predetermined specific object may include information about at least one of the shape, size, and/or category of the predetermined specific object. The predetermined property information of a specific object may include information about the physical properties of the predetermined specific object. For example, property information of a predetermined specific object may include information about collision of the predetermined specific object. Accordingly, the processor 110 sets the predetermined specific object information applied to the environment of the collision map based on the predetermined specific object information included in the state information (for example, a specification of the predetermined specific object information). , location, etc.) can be determined. However, the predetermined specific object information is not limited to this and may include various predetermined specific object information.

The interaction information may include information about interactions that occur based on two or more of camera information, moving object information, date information, lighting information, weather information, and/or predetermined specific object information in the collision map. . For example, interaction information may include visual effects applied based on weather information in a collision map. As an example, the interaction information may include at least one of whether rain and/or snow accumulates on the moving object and/or a specific predetermined object in the collision map, and the rate of evaporation and/or melting.

In some embodiments of the present disclosure, when arranging virtual lighting on a collision map, the computing device 100 provides a first virtual brightness or a first virtual brightness according to the first virtual brightness on the collision map based on information on the virtual lighting. 1 At least one of the visual effects can be determined (340).

The virtual lighting information may include at least one of virtual lighting location information, virtual lighting direction information, and/or virtual lighting characteristic information. The location information of the virtual light may include location coordinates where the virtual light exists. The direction information of the virtual light may include at least one of the direction and/or angle from which the virtual light is facing. The characteristic information of the virtual lighting may include information related to at least one of the color, brightness, reach, or shadow caused by the virtual lighting.

The first visual effect may include an effect corresponding to at least one of overexposure, backlight, dark adaptation, glare, or bokeh. Overexposure may occur when there is excessive exposure to light from at least one of virtual lighting and/or natural lighting. Overexposure can cause colors to appear brighter than before. Backlighting may cause light from at least one of virtual lighting and/or natural lighting to be strongly projected onto the back of an object, making the object appear to have relatively low brightness. Dark adaptation is the effect of the eye adapting to a dark place, so that the shape of an object can be identified in a dark place rather than the object appearing black.

In some embodiments of the present disclosure, when placing a virtual object on a collision map, the computing device 100 determines the second virtual brightness or darkness on the collision map based on the information of the virtual lighting and the information of the virtual object. At least one of the second visual effects according to light and dark can be determined.

The virtual object information may include at least one of virtual object location information, virtual object direction information, and virtual object material information.

The location information of the virtual object may include location coordinates where the virtual object exists.

The direction information of the virtual object may include at least one of the direction and/or angle from which the virtual object is facing.

Material information of a virtual object may include information about physical elements of the virtual object. For example, the material information of the virtual object may include at least one of temperature, viscosity, absorption rate, reflectance, transmittance, scattering rate, attenuation rate, elastic coefficient, and/or friction coefficient at the volume or surface of the object. The volume of a virtual object may be a three-dimensional area excluding the surface of the object. For example, if the virtual object is in the form of a gas, the volume of the virtual object may mean the area occupied by the gas.

After determining a collision map (330), the computing device 100 may obtain information about real-world lighting related to the user device by comparing the query image and the collision map.

For example, by comparing the query image and the collision map, determining the difference in light and dark on the query image and the collision map, and using the determined light and dark difference to illuminate the reality on the query image that does not exist in the collision map. information can be estimated.

The computing device 100 may determine information on complex lighting based on information on virtual lighting and information on real lighting. The information about the real lighting may include at least one of location information of the real lighting, direction information of the real lighting, and/or characteristic information of the real lighting. The location information of real lighting may include location coordinates where real lighting exists. Direction information of real-world lighting may include at least one of the direction and/or angle that the real-world lighting faces. The characteristic information of real lighting may include information related to at least one of the color, brightness, reach, or shadow caused by real lighting.

For example, the computing device 100 may estimate complex lighting information using estimated real lighting information, virtual lighting position information, virtual lighting direction information, and virtual lighting characteristic information.

The computing device 100 may determine a third virtual brightness and a third visual effect according to the third virtual brightness on the collision map based on information on the complex lighting.

When placing a virtual object on the collision map, the computing device 100 displays the fourth virtual brightness and/or the fourth visual brightness according to the fourth virtual brightness on the collision map based on the information of the complex lighting and the information of the virtual object. At least one of the effects can be determined.

When placing virtual sound on the collision map, the computing device 100 may determine the first auditory effect applied to the user device on the collision map based on information on the virtual sound. The virtual sound information may include at least one of virtual sound location information, virtual sound direction information, and/or virtual sound characteristic information. The location information of the virtual sound may include location coordinates where the virtual sound exists. The direction information of the virtual sound may include at least one of the direction and/or angle from which the virtual sound is viewed. The characteristic information of the virtual sound may include information related to the reach of the virtual sound (eg, sound volume of the virtual sound, sound wave, etc.).

The computing device 100 may determine the first auditory effect by calculating the distance from the virtual sound to the user device using a sound attenuation function. The sound attenuation function is an Attenuation Linear model in which the volume according to distance decreases in direct proportion, an Attenuation Logarithmic model in which the volume according to distance decreases logarithmically, an Attenuation LogReverse model in which the volume according to distance decreases in an inverse exponential form, and a specific equation ( For example, it may include an Attenuation Inverse model using (UsedMaxRadius/UsedMinRadius)*(0.02/(Distance/UsedMaxRadius), etc.).

When placing a virtual object on a collision map, the computing device 100 may determine a second auditory effect applied to the user device on the collision map based on virtual sound information and virtual object information.

Figure 4 exemplarily shows a method for estimating a pose through visual localization according to an embodiment of the present disclosure.

The computing device 100 uses the pre-trained feature point acquisition model 420 to obtain at least one feature point 430 and 440 from the query data (e.g., query image, depth map, etc.) 410. At least one descriptor corresponding to each feature point 430 and 440 may be obtained. For example, the feature points 430 and 440 may be coordinates for each of the characteristic parts or points on the query image and/or depth map of the query data 410. For example, the descriptor may include at least one of information about the directionality and size of each feature point, and/or the relationship between pixels surrounding each feature point.

In one embodiment, the feature point acquisition model 420 is based on supervised learning to recognize as a feature point a point where the amount of change in at least one of color or geometric pattern in an object within the query image or depth map exceeds a predetermined threshold. Thus, a pre-trained model can be included. For example, the feature point acquisition model 420 may correspond to a model pre-trained using an image-based neural network. As another example, the feature point acquisition model 420 may correspond to a pre-trained model using a transformer-based neural network.

In one embodiment, the query data 410 includes a query image obtained by the user device, a descriptor corresponding to the query image, a depth map obtained by the user device, a descriptor corresponding to the depth map, and/or additional information of the user device. may include.

In one embodiment, the computing device 100 may perform visual localization 450 based on the first feature points 430 corresponding to the query image of the query data 410. The computing device 100 may acquire first feature points 430 for the query image included in the query data 410 using an artificial intelligence-based feature point acquisition model 420. The computing device 100 may perform visual localization 450 based on the first feature points 430, descriptors corresponding to the first feature points 430, and a plurality of pre-stored reference images. As a result of this visual localization 450, the pose 460 for the user device can be estimated. The pose 460 for the user device is determined based on the pose for the HMD, AR glasses, or VR glasses corresponding to the user device. It may include the location and direction of the helmet.

In one embodiment, visual localization 450 may include a methodology for determining the location and facing direction of a user device on query data based on a comparison between the query data and a pre-stored reference image. Specific methodologies for visual localization 450 are described in Korean Patent Application No. 10-2022-0081908 filed on July 4, 2022, which is incorporated by reference into this patent application.

In one embodiment, the computing device 100 may perform visual localization 450 based on the second feature points 440 corresponding to the depth map of the query data 410. The computing device 100 may acquire second feature points 440 for the depth map included in the query data 410 using an artificial intelligence-based feature point acquisition model 420. The computing device 100 performs the visual localization 450 based on the second feature points 440, descriptors corresponding to the second feature points 440, and a plurality of pre-stored reference images, thereby providing visual localization to the user device. The pose 460 can be determined.

In one embodiment, the computing device 100 performs visual localization 450 based on the first feature points 430 corresponding to the query image of the query data 410 and the second feature points 440 corresponding to the depth map. can be performed. For example, the computing device 100 compares the first feature points 430 and the second feature points 440 to determine pixel coordinates corresponding to each other among the first feature points 430 and the second feature points 440. Third feature points having values can be determined. For example, the third feature points may include overlapping feature points among the first feature points 430 and the second feature points 440 . The computing device 100 may determine reference images having feature points corresponding to third feature points among a plurality of pre-stored reference images, and determine a pose 460 for the user device based on the determined reference images. . As another example, the computing device 100 compares first feature points 430 and descriptors corresponding to the first feature points 430 with a pre-stored reference image and compares the second feature points 440 and the second Visual localization 450 may be performed by comparing descriptors corresponding to the feature points 440 with a pre-stored reference image.

In one embodiment, when the computing device 100 performs visual localization 450 for the user device that obtained the query data 410, the computing device 100's database (e.g., memory 130, etc.) ) Visual localization 450 for the user device that obtained the query data 410 based on the reference image stored in the stored reference image, the feature points 430 and/or 440 on the query data 410, and additional information of the user device. It can be done.

In one embodiment, when performing visual localization 450 on a user device that has acquired query data 410, the computing device 100 selects query image feature points and feature points of the reference image from among a plurality of reference images. A pose reference image can be determined based on matching between the pose reference images. The computing device 100 determines the determined pose reference image according to a comparison result between the 3D camera coordinates assigned to the pose reference image and the coordinates (e.g., two-dimensional coordinates) of the feature points 430 and/or 440 of the query data, Camera pose information for the query data 410 may be determined. The pose reference image in the present disclosure may refer to a reference image that is subject to matching with the query data 410 among reference images. For example, the pose reference image may mean an image with the highest matching rate with the query data 410 among a plurality of candidate images to be compared with the query data 410. In this example, computing device 100 may perform visual localization on query data 410 using 3D camera coordinates assigned to the pose reference image.

In one embodiment, computing device 100, when performing visual localization 450 for the user device that obtained query data 410, uses camera pose information (e.g., 3D camera coordinates) assigned to the pose reference image. and determine absolute camera pose information of the query image with respect to the origin based on relative camera pose information for the query data 410 determined through the pose reference image, and determine query data 410 based on the absolute camera pose information. Visual localization 450 can be performed on the acquired user device.

Absolute camera pose information in the present disclosure may be, for example, a geodetic datum or geodetic system (or geodetic reference datum, geodetic reference system, or geodetic reference frame), spatial reference system (SRS) used in a specific region or a specific country. ) or coordinate reference system (CRS), or geographic coordinate system (GCS), etc. may include coordinate values and/or direction values. Relative camera pose information in the present disclosure may include camera pose information generated by a comparison result between 3D coordinates of camera pose information assigned to a reference image and 2D coordinates between feature points of the query data 410.

In one embodiment, when the computing device 100 obtains a feature point and extracts the feature points 430 and/or 440 for the query data 410 from the query data 410, the feature points 430 and/or 440), and the corresponding pixel coordinates and descriptor can be obtained. The computing device 100 obtains the query data 410 by comparing the descriptor corresponding to the feature points 430 and/or 440 of the query data 410 with the descriptor corresponding to the feature points of the reference image stored in the database. Visual localization 450 can be performed for one user device. The descriptors are mapped to the query data 410 and can be compared with the descriptors mapped to the reference image to determine a candidate reference image to be compared with the query data 410 among the plurality of reference images. Descriptors can be used to efficiently perform a comparison between the reference image and query data 410.

In one embodiment, the computing device 100 compares the descriptor corresponding to the feature points of the query data 410 with the descriptor corresponding to the feature points of the reference image stored in the database, thereby providing information about the user device among the plurality of reference images. Determine a pose reference image for visual localization 450, and apply 3D coordinate information mapped to the pose reference image, matching information between feature points, and feature points 430 and/or 440 of the query data 410. Based on the corresponding pixel coordinates, visual localization 450 including camera pose estimation for the user device may be performed.

In the manner described above, a technique according to an embodiment of the present disclosure applies visual localization 450 (e.g., pose estimation for a VPS-based user device) to the output of the artificial intelligence-based feature point acquisition model 420. , the pose 460 of the user device that transmitted the query data 410 can be estimated in an accurate manner.

Figures 5 and 6 exemplarily show a method for implementing a physics engine in a collision map using visual localization according to an embodiment.

Referring to FIG. 5 , the computing device 100 may provide an augmented reality and/or virtual reality platform based on a collision map. The computing device 100 may estimate the current location and direction of the user device 520 by obtaining captured data (eg, query image, etc.) from the user device 520 equipped with a photographing function. The computing device 100 may provide an augmented reality and/or virtual reality platform based on the collision map to the client 510 using the user device 520 by estimating the location and direction of the user device 520. .

For example, computing device 100 may determine a collision map corresponding to the estimated current location and direction of user device 520. The computing device 100 is a virtual object on a real-world terrain feature 530, a real-life lighting 560, and a real-world reflective surface (or transmission surface) 570, which are objects included in a collision map corresponding to reality. A virtual moving object (or, floating object) 540, a virtual light 550, a virtual reflective surface (or a transparent surface) 580, and an augmented reality and/or virtual reality platform with virtual sound added to the user's device. It can be provided through (520).

For example, referring to FIG. 6, when a virtual object (or real object) 610 is reflected on a virtual reflective surface (or real reflective surface) 620, the computing device 100 displays a virtual reflective surface (or real reflective surface) 620. In 620), the reflected image of the virtual object (or the reflected image of the real object) may be added to the collision map to be displayed on the user device 520.

Detailed information regarding the configurations (eg, configuration of the computing device 100, etc.) depicted in FIGS. 4 to 6 may be replaced with the content previously described with reference to FIGS. 1 to 3 .

As described above with reference to FIGS. 1 to 6, the computing device 100 provides an augmented reality and/or virtual reality platform according to a collision map through the user's device, providing the client with virtual objects and virtual reality on the real terrain. It can give a feeling of realism, as if the sound was actually added.

In addition, the computing device 100 determines a collision map corresponding to the result of visual localization, thereby quickly adding virtual objects and virtual sounds with fewer resources than when loading the entire collision map and adding virtual objects and virtual sounds. This added collision map can be created and provided to the user.

In addition, the computing device 100 uses an artificial intelligence-based classification or segmentation model to automatically reflect the material information about the object in the virtual reality space without manually mapping the material information about the object. All maps can be created efficiently and quickly and provided to users.

7 is a brief, general schematic diagram of an example computing environment in which embodiments of the present disclosure may be implemented.

Although the present disclosure has generally been described above as being capable of being implemented by a computing device, those skilled in the art will understand that the present disclosure can be implemented in combination with computer-executable instructions and/or other program modules that can be executed on one or more computers and/or in hardware and software. It will be well known that it can be implemented as a combination.

Typically, program modules include routines, programs, components, data structures, etc. that perform specific tasks or implement specific abstract data types. Additionally, those skilled in the art will understand that the methods of the present disclosure are applicable to single-processor or multiprocessor computer systems, minicomputers, mainframe computers, as well as personal computers, handheld computing devices, microprocessor-based or programmable consumer electronics, etc. It will be appreciated that each of these may be implemented in other computer system configurations, including those capable of operating in conjunction with one or more associated devices.

The described embodiments of the disclosure can also be practiced in distributed computing environments where certain tasks are performed by remote processing devices that are linked through a communications network. In a distributed computing environment, program modules may be located in both local and remote memory storage devices.

Computers typically include a variety of computer-readable media. Computer-readable media can be any medium that can be accessed by a computer, and such computer-readable media includes volatile and non-volatile media, transitory and non-transitory media, removable and non-transitory media. Includes removable media. By way of example, and not limitation, computer-readable media may include computer-readable storage media and computer-readable transmission media. Computer-readable storage media refers to volatile and non-volatile media, transient and non-transitory media, removable and non-removable, implemented in any method or technology for storage of information such as computer readable instructions, data structures, program modules or other data. Includes media. Computer readable storage media may include RAM, ROM, EEPROM, flash memory or other memory technology, CD-ROM, digital video disk (DVD) or other optical disk storage, magnetic cassette, magnetic tape, magnetic disk storage or other magnetic storage. This includes, but is not limited to, a device, or any other medium that can be accessed by a computer and used to store desired information.

A computer-readable transmission medium typically implements computer-readable instructions, data structures, program modules, or other data on a modulated data signal, such as a carrier wave or other transport mechanism. Includes all information delivery media. The term modulated data signal refers to a signal in which one or more of the characteristics of the signal have been set or changed to encode information within the signal. By way of example, and not limitation, computer-readable transmission media includes wired media such as a wired network or direct-wired connection, and wireless media such as acoustic, RF, infrared, and other wireless media. Combinations of any of the above are also intended to be included within the scope of computer-readable transmission media.

An example environment 1100 is shown that implements various aspects of the present disclosure, including a computer 1102, which includes a processing unit 1104, a system memory 1106, and a system bus 1108. do. System bus 1108 couples system components, including but not limited to system memory 1106, to processing unit 1104. Processing unit 1104 may be any of a variety of commercially available processors. Dual processors and other multiprocessor architectures may also be used as processing unit 1104.

System bus 1108 may be any of several types of bus structures that may further be interconnected to a memory bus, peripheral bus, and local bus using any of a variety of commercial bus architectures. System memory 1106 includes read only memory (ROM) 1110 and random access memory (RAM) 1112. The basic input/output system (BIOS) is stored in non-volatile memory 1110, such as ROM, EPROM, and EEPROM, and is a basic input/output system that helps transfer information between components within the computer 1102, such as during startup. Contains routines. RAM 1112 may also include high-speed RAM, such as static RAM, for caching data.

Computer 1102 may also include an internal hard disk drive (HDD) 1114 (e.g., EIDE, SATA)—the internal hard disk drive 1114 may also be configured for external use within a suitable chassis (not shown). Yes - a magnetic floppy disk drive (FDD) 1116 (e.g., for reading from or writing to a removable diskette 1118), and an optical disk drive 1120 (e.g., a CD-ROM for reading the disk 1122 or reading from or writing to other high-capacity optical media such as DVDs). Hard disk drive 1114, magnetic disk drive 1116, and optical disk drive 1120 are connected to system bus 1108 by hard disk drive interface 1124, magnetic disk drive interface 1126, and optical drive interface 1128, respectively. ) can be connected to. The interface 1124 for implementing an external drive includes at least one or both of Universal Serial Bus (USB) and IEEE 1394 interface technologies.

These drives and their associated computer-readable media provide non-volatile storage of data, data structures, computer-executable instructions, and the like. For computer 1102, drive and media correspond to storing any data in a suitable digital format. Although the description of computer-readable media above refers to removable optical media such as HDDs, removable magnetic disks, and CDs or DVDs, those skilled in the art will also recognize removable optical media such as zip drives, magnetic cassettes, flash memory cards, cartridges, etc. It will be appreciated that other types of computer-readable media, such as the like, may also be used in the example operating environment and that any such media may contain computer-executable instructions for performing the methods of the present disclosure.

A number of program modules may be stored in drives and RAM 1112, including an operating system 1130, one or more application programs 1132, other program modules 1134, and program data 1136. All or portions of the operating system, applications, modules and/or data may also be cached in RAM 1112. It will be appreciated that the present disclosure may be implemented on various commercially available operating systems or combinations of operating systems.

A user may enter commands and information into computer 1102 through one or more wired/wireless input devices, such as a keyboard 1138 and a pointing device such as mouse 1140. Other input devices (not shown) may include microphones, IR remote controls, joysticks, game pads, stylus pens, touch screens, etc. These and other input devices are connected to the processing unit 1104 through an input device interface 1142, which is often connected to the system bus 1108, but may also include a parallel port, an IEEE 1394 serial port, a game port, a USB port, an IR interface, It can be connected by other interfaces, etc.

A monitor 1144 or other type of display device is also connected to system bus 1108 through an interface, such as a video adapter 1146. In addition to monitor 1144, computers typically include other peripheral output devices (not shown) such as speakers, printers, etc.

Computer 1102 may operate in a networked environment using logical connections to one or more remote computers, such as remote computer(s) 1148, via wired and/or wireless communications. Remote computer(s) 1148 may be a workstation, computing device computer, router, personal computer, portable computer, microprocessor-based entertainment device, peer device, or other conventional network node, and is generally connected to computer 1102. For simplicity, only memory storage device 1150 is shown, although it includes many or all of the components described. The logical connections depicted include wired/wireless connections to a local area network (LAN) 1152 and/or a larger network, such as a wide area network (WAN) 1154. These LAN and WAN networking environments are common in offices and companies and facilitate enterprise-wide computer networks, such as intranets, all of which can be connected to a worldwide computer network, such as the Internet.

When used in a LAN networking environment, computer 1102 is connected to local network 1152 through wired and/or wireless communication network interfaces or adapters 1156. Adapter 1156 may facilitate wired or wireless communication to LAN 1152, which also includes a wireless access point installed thereon for communicating with wireless adapter 1156. When used in a WAN networking environment, the computer 1102 may include a modem 1158 or be connected to a communicating computing device on the WAN 1154 or to establish communications over the WAN 1154, such as over the Internet. Have other means. Modem 1158, which may be internal or external and a wired or wireless device, is coupled to system bus 1108 via serial port interface 1142. In a networked environment, program modules described for computer 1102, or portions thereof, may be stored in remote memory/storage device 1150. It will be appreciated that the network connections shown are exemplary and that other means of establishing a communications link between computers may be used.

Computer 1102 may be associated with any wireless device or object deployed and operating in wireless communications, such as a printer, scanner, desktop and/or portable computer, portable data assistant (PDA), communications satellite, wirelessly detectable tag. Performs actions to communicate with any device or location and telephone. This includes at least Wi-Fi and Bluetooth wireless technologies. Accordingly, communication may be a predefined structure as in a conventional network or may simply be ad hoc communication between at least two devices.

Wi-Fi (Wireless Fidelity) allows connection to the Internet, etc. without wires. Wi-Fi is a wireless technology, like cell phones, that allows these devices, such as computers, to send and receive data indoors and outdoors, anywhere within the coverage area of a base station. Wi-Fi networks use wireless technology called IEEE 802.11 (a, b, g, etc.) to provide secure, reliable, and high-speed wireless connections. Wi-Fi can be used to connect computers to each other, the Internet, and wired networks (using IEEE 802.3 or Ethernet). Wi-Fi networks can operate in the unlicensed 2.4 and 5 GHz wireless bands, for example, at data rates of 11 Mbps (802.11a) or 54 Mbps (802.11b), or in products that include both bands (dual band). .

Those skilled in the art will understand that information and signals may be represented using any of a variety of different technologies and techniques. For example, data, instructions, commands, information, signals, values, symbols and chips that may be referenced in the above description include voltages, currents, electromagnetic waves, magnetic fields or particles, optical fields. or particles, or any combination thereof.

Those skilled in the art will understand that the various illustrative logical blocks, modules, processors, means, circuits and algorithm steps described in connection with the embodiments disclosed herein may be used in electronic hardware, (for convenience) It will be understood that it may be implemented by various forms of program or design code (referred to herein as software) or a combination of both. To clearly illustrate this interoperability of hardware and software, various illustrative components, blocks, modules, circuits and steps have been described above generally with respect to their functionality. Whether this functionality is implemented as hardware or software depends on the specific application and design constraints imposed on the overall system. A person skilled in the art of this disclosure may implement the described functionality in various ways for each specific application, but such implementation decisions should not be construed as departing from the scope of this disclosure.

The various embodiments presented herein may be implemented as a method, apparatus, or article of manufacture using standard programming and/or engineering techniques. The term article of manufacture includes a computer program, carrier, or media accessible from any computer-readable storage device. For example, computer-readable storage media include magnetic storage devices (e.g., hard disks, floppy disks, magnetic strips, etc.), optical disks (e.g., CDs, DVDs, etc.), smart cards, and flash. Includes, but is not limited to, memory devices (e.g., EEPROM, cards, sticks, key drives, etc.). Additionally, various storage media presented herein include one or more devices and/or other machine-readable media for storing information.

It is to be understood that the specific order or hierarchy of steps in the processes presented is an example of illustrative approaches. It is to be understood that the specific order or hierarchy of steps in processes may be rearranged within the scope of the present disclosure, based on design priorities. The appended method claims present elements of the various steps in a sample order but are not meant to be limited to the particular order or hierarchy presented.

The description of the presented embodiments is provided to enable any person skilled in the art to make or use the present disclosure. Various modifications to these embodiments will be apparent to those skilled in the art, and the general principles defined herein may be applied to other embodiments without departing from the scope of the disclosure. Thus, the present disclosure is not limited to the embodiments presented herein but is to be interpreted in the broadest scope consistent with the principles and novel features presented herein.

Claims (20)

A method for placing virtual lighting through visual localization performed by a computing device, comprising:
Receiving a query image obtained from a user device and a depth map corresponding to the query image;
Obtaining first feature points corresponding to the query image from the query image using a pre-trained first feature point acquisition model;
Obtaining second feature points corresponding to the depth map from the depth map using a pre-trained second feature point acquisition model;
Comparing the first feature points and the second feature points to obtain third feature points having corresponding pixel coordinate values;
Obtaining a result of visual localization by comparing the first feature points, the second feature points, and the third feature points with each of a plurality of pre-stored reference images; and
Determine a collision map including information on virtual lighting using the result of the visual localization, determine a first virtual brightness on the collision map based on the information on the virtual lighting, and determine the first virtual brightness on the first virtual lighting. determining a first visual effect;
Including,
The first visual effect includes an effect corresponding to at least one of overexposure, backlight, glare, or bokeh,
method.
According to claim 1,
The result of the visual localization is:
a pose including the relative position and orientation of the user device;
Including,
method.
According to claim 1,
The collision map is,
Generated based on information about the space associated with the query image, scanned or photographed using an imaging device,
method.
According to claim 1,
The collision map is,
Information representing physical phenomena is reflected in advance,
method.
According to claim 4,
The information representing the physical phenomenon is,
Containing at least one of material information or spatial information of the object included in the collision map,
method.
According to claim 5,
The material information of the object is,
Containing at least one of temperature, viscosity, absorption rate, reflectance, transmittance, scattering rate, attenuation rate, elastic coefficient, or friction coefficient at the volume or surface of the object,
method.
According to claim 5,
The spatial information is,
Gravity field, magnetic field, electric field, Coriolis force, wind direction, wind speed, temperature, humidity, air resistance, altitude above sea level, air density, viscosity, scattering rate, atmospheric pressure, elastic coefficient, friction coefficient, vector field or tensor field in space. Containing at least one of (tensor fields),
method.
According to claim 1,
The information of the virtual lighting is,
Containing at least one of location information of the virtual lighting, direction information of the virtual lighting, or characteristic information of the virtual lighting,
method.
According to claim 8,
The characteristic information of the virtual lighting is,
Containing information related to at least one of the color, brightness, reach, or shadow caused by the virtual light of the virtual light,
method.
delete According to claim 1,
When placing a virtual object on the collision map, at least one of a second virtual light and darkness on the collision map or a second visual effect according to the second virtual light and dark based on the information of the virtual lighting and the information of the virtual object. determining;
Containing more,
method.
According to claim 11,
The information of the virtual object is,
Containing at least one of location information of the virtual object, direction information of the virtual object, or material information of the virtual object,
method.
According to claim 12,
The material information of the virtual object is,
Containing at least one of temperature, viscosity, absorption rate, reflectance, transmittance, scattering rate, attenuation rate, elastic coefficient, or friction coefficient at the volume or surface of the virtual object,
method.
According to claim 1,
After determining the collision map,
Comparing the query image and the collision map to obtain information about real-world lighting related to the user device;
determining information on complex lighting based on the information on the virtual lighting and the information on the real lighting; and
determining a third virtual brightness and a third visual effect according to the third virtual brightness and darkness on the collision map based on the information on the complex lighting;
Containing more,
method.
According to claim 14,
Comparing the query image and the collision map to obtain information about real-world lighting related to the user device,
Compare the query image and the collision map to determine the difference in brightness on the query image and the collision map, and use the determined brightness difference to determine the difference in brightness on the query image that does not exist in the collision map. estimating information of real lighting;
Includes, and
The step of determining information on complex lighting based on the information on the virtual lighting and the information on the real lighting,
estimating information on the composite lighting using the estimated real lighting information, location information on the virtual lighting, direction information on the virtual lighting, and characteristic information on the virtual lighting;
Including,
method.
According to claim 14,
When placing a virtual object on the collision map, at least one of a fourth virtual brightness or a fourth visual effect according to the fourth virtual brightness on the collision map based on the information of the complex lighting and the information of the virtual object. determining;
Containing more,
method.
According to claim 1,
When placing a virtual sound on the collision map, determining a first auditory effect applied to the user device on the collision map based on information on the virtual sound;
Containing more,
method.
According to claim 17,
When placing a virtual object on the collision map, determining a second auditory effect applied to the user device on the collision map based on information on the virtual sound and information on the virtual object;
Containing more,
method.
A computer program stored in a computer-readable storage medium, the computer program comprising instructions for causing a processor of a computing device to perform the following steps for arranging virtual lighting through visual localization, the steps comprising: :
Receiving a query image obtained from a user device and a depth map corresponding to the query image;
Obtaining first feature points corresponding to the query image from the query image using a pre-trained first feature point acquisition model;
Obtaining second feature points corresponding to the depth map from the depth map using a pre-trained second feature point acquisition model;
Comparing the first feature points and the second feature points to obtain third feature points having corresponding pixel coordinate values;
Obtaining a result of visual localization by comparing the first feature points, the second feature points, and the third feature points with each of a plurality of pre-stored reference images; and
Determine a collision map including information on virtual lighting using the result of the visual localization, determine a first virtual brightness on the collision map based on the information on the virtual lighting, and determine the first virtual brightness on the first virtual lighting. determining a first visual effect;
Including,
The first visual effect includes an effect corresponding to at least one of overexposure, backlight, glare, or bokeh,
A computer program stored on a computer-readable storage medium.
In a computing device for arranging virtual lighting through visual localization,
A processor including at least one core;
a memory storing a computer program executable by the processor; and
network department;
Including,
The processor,
Receiving a query image obtained from a user device and a depth map corresponding to the query image,
Obtaining first feature points corresponding to the query image from the query image using a pre-trained first feature point acquisition model,
Obtaining second feature points corresponding to the depth map from the depth map using a pre-trained second feature point acquisition model,
Comparing the first feature points and the second feature points to obtain third feature points having corresponding pixel coordinate values,
Obtain a result of visual localization by comparing the first feature points, the second feature points, and the third feature points with each of a plurality of pre-stored reference images, and
Determine a collision map including information on virtual lighting using the result of the visual localization, determine a first virtual brightness on the collision map based on the information on the virtual lighting, and determine the first virtual brightness on the first virtual lighting. Determine the first visual effect according to
The first visual effect includes an effect corresponding to at least one of overexposure, backlight, glare, or bokeh,
Computing device.

Images