KR102616082B1 - Apparatus and method for deciding validity of camera pose using visual localization - Google Patents

Abstract

According to some embodiments of the present disclosure, a method for visual localization performed by a computing device including a processor, comprising: obtaining a first depth map; Obtaining first depth map feature point information from the first depth map using a pre-learned depth map feature point acquisition model; Comparing the first depth map feature point information with reference depth map feature point information of a pre-stored reference depth map to obtain depth map comparison data; estimating a camera pose of a device associated with the first depth map based on the depth map comparison data; Placing a virtual device corresponding to the camera pose on a pre-generated 3D model; Obtaining a second depth map using the virtual device; and comparing the first depth map and the second depth map to determine an effective value of the camera pose.

Description

Method and apparatus for determining validity of camera pose using visual localization {APPARATUS AND METHOD FOR DECIDING VALIDITY OF CAMERA POSE 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.

Republic of Korea registered patent 10-2225093 (registered on March 3, 2021)

This disclosure has been made in response to the above-described background technology, and is intended to determine the effectiveness of a camera pose 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, there is provided a method for visual localization performed by a computing device including a processor, the method comprising: obtaining a first depth map; Obtaining first depth map feature point information from the first depth map using a pre-learned depth map feature point acquisition model; Comparing the first depth map feature point information with reference depth map feature point information of a pre-stored reference depth map to obtain depth map comparison data; estimating a camera pose of a device associated with the first depth map based on the depth map comparison data; Placing a virtual device corresponding to the camera pose on a pre-generated 3D model; Obtaining a second depth map using the virtual device; and comparing the first depth map and the second depth map to determine an effective value of the camera pose.

Alternatively, obtaining the first depth map may include obtaining an image in a format other than a depth map from the device; and acquiring a first depth map from the image using a pre-learned depth map acquisition model.

Alternatively, acquiring the depth map comparison data may include matching reference depth map feature point information of each of a plurality of pre-stored reference depth maps with the first depth map feature point information; determining a matching reference depth map that matches the first depth map feature point information from among the plurality of pre-stored reference depth maps; and obtaining the depth map comparison data by comparing matching reference depth map feature point information of the matching reference depth map with the first depth map feature point information.

Alternatively, estimating the camera pose may include estimating the camera pose based on 2D coordinate values and 3D coordinate values of each of a plurality of matching feature points included in the depth map comparison data. there is.

Alternatively, the 2D coordinate values may include two-dimensional pixel coordinate values in the first depth map.

Alternatively, the 3D coordinate values may include coordinate values in three dimensions in the coordinate system of a reference device associated with the reference depth map.

Alternatively, the pre-generated 3D model may be generated based on spatial information about a space associated with the first depth map scanned or photographed using an imaging device.

Alternatively, determining the effective value of the camera pose may include obtaining first pixel-wise difference values from the first depth map and the second depth map using a pixel-wise operation; and determining the number of difference values corresponding to a predetermined first standard among the difference values for each first pixel as an effective value of the camera pose.

Alternatively, determining the effective value of the camera pose may include obtaining second pixel-specific difference values from the first depth map and the second depth map using a neighborhood operation; and determining the number of difference values corresponding to a predetermined second standard among the second pixel-specific difference values as an effective value of the camera pose.

Alternatively, the method may include re-estimating the camera pose when the effective value of the camera pose corresponds to a predetermined third criterion.

Alternatively, the pre-trained depth map feature point acquisition model may be learned in advance through deep learning or machine learning to acquire feature points from a specific depth map included in specific input data.

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 being: 1 Obtaining a depth map; Obtaining first depth map feature point information from the first depth map using a pre-learned depth map feature point acquisition model; Comparing the first depth map feature point information with reference depth map feature point information of a pre-stored reference depth map to obtain depth map comparison data; estimating a camera pose of a device associated with the first depth map based on the depth map comparison data; Placing a virtual device corresponding to the camera pose on a pre-generated 3D model; Obtaining a second depth map using the virtual device; and comparing the first depth map and the second depth map to determine an effective value of the camera pose.

Alternatively, a computing device for visual localization comprising: a processor comprising at least one core; a memory storing a computer program executable by the processor; and a network unit, wherein the processor acquires a first depth map, uses a pre-learned depth map feature point acquisition model, and obtains first depth map feature point information from the first depth map, and the first 1 Obtain depth map comparison data by comparing depth map feature point information with reference depth map feature point information of a pre-stored reference depth map, and estimate a camera pose of the device related to the first depth map based on the depth map comparison data; , place a virtual device corresponding to the camera pose in a pre-generated 3D model, obtain a second depth map using the virtual device, and compare the first depth map and the second depth map to determine the camera The effective value of the pose can be determined.

A technique according to an embodiment of the present disclosure can determine the effectiveness of a camera pose 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 illustrates a system for determining the validity of a camera pose using visual localization in accordance with some embodiments of the present disclosure.
Figure 2 is a schematic diagram illustrating network functions according to some embodiments of the present disclosure.
FIG. 3 is a diagram illustrating a process for determining an effective value of a camera pose using pixel-wise calculation by a computing device according to some embodiments of the present disclosure.
FIG. 4 is a diagram illustrating a process of determining an effective value of a camera pose using a neighborhood operation by a computing device according to some embodiments of the present disclosure.
FIG. 5 is a flowchart illustrating a method for determining the effectiveness of a camera pose using visual localization according to some embodiments of the present disclosure.
FIG. 6 is a flowchart illustrating a method for determining the effectiveness of a camera pose using visual localization according to several other embodiments of the present disclosure.
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,” “module,” “system,” and the like 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. Terms expressed as 1-1, 1-2, and 2-1, 2-2 may also be used to distinguish a plurality of entities.

In the present disclosure, visual localization refers to a technology for estimating the current location or pose of a device using images taken indoors or outdoors. Pose estimation for a user device (or camera) refers to determining translation and rotation information of a dynamically changing camera viewpoint. As an exemplary implementation method rather than a limitation on visual localization, a Visual Positioning System (VPS) can be used.

The query image in the query data in the present disclosure includes an image captured by a device, and a reference image stored in a computing device (e.g., server, etc.) and a device corresponding to the query image using the query image. The pose of can be determined or estimated.

A reference image in the present disclosure may include a pre-stored image used for pose estimation of the device that captured the query image. The reference image may include actual images taken in a specific area to implement, for example, augmented reality or virtual reality. These reference images may be stored on the computing device. Metadata mapped to the reference image can also be used to estimate the pose of the user device.

Metadata that may be included in query data in the present disclosure may mean additional information used in performing visual localization. For example, metadata may include additional information other than the image, such as information related to the device, pixel coordinate information of feature points, descriptor of feature points, landmark information obtained from the image, character information obtained from the image, etc. . For example, by utilizing metadata of the query image and/or reference image, the candidate group of reference images to be matched with the query image may be reduced. As another example, by utilizing metadata of the query image and/or reference image, a more suitable camera pose estimation method may be used.

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.

The relative pose and/or relative camera pose in the present disclosure may include pose information in a relative concept based on the pose determined by visual localization. For example, a pose determined by visual localization may be referred to as an absolute pose, and a relative coordinate value using the 3D coordinate value of the absolute pose as a reference point may correspond to a relative pose and/or a relative camera pose.

1 schematically illustrates a system for determining the validity of a camera pose using visual localization in accordance with some embodiments of the present disclosure.

Computing device 100 according to an embodiment of the present disclosure may include a processor 110 and a memory 130.

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 terminal or any type of server.

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 an additional embodiment, the above-described server may include a storage unit that stores and manages a reference image, metadata corresponding to the reference image, a reference depth map, metadata corresponding to the reference depth map, a 3D map, and a 3D model. 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.

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 terminal, an output unit (not shown) and an input unit (not shown) may be included within the scope of the computing device 100.

Computing device 100 in this disclosure may include, for example, a digital twin server and/or a VPS server.

A user device (eg, device 200, etc.) in the present disclosure may refer to a device for generating query data. Query data is information captured by a user device and may include a query image (eg, a first image, etc.) and/or a query depth map (eg, a first depth map, etc.). The query image captured by the user device may be used for comparison with a reference image within a computing device (eg, a server), and thus the pose of the user device at the time of capture may be determined. Additionally, the query depth map captured at the user device may be used for comparison with a reference depth map within a computing device (eg, a server), and thus the pose at the capture point of the user device may be determined.

A user device (e.g., device 200, etc.) in this disclosure may include any type of mobile device, including a camera 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 (e.g., device 200, etc.) and the computing device 100 are distinguished, and the computing device 100 receives shooting information from the user device, The methodology for visual localization to estimate pose will be explained. However, embodiments that perform visual localization on a user device that generates query data 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.

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).

Processor 110 may read a computer program stored in memory 130 and perform methodologies to support visual localization methodologies according to an embodiment of the present disclosure. Additionally, the processor 110 may read a computer program stored in the memory 130 and perform methods for supporting visual localization and/or tactical training 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 storage to provide information or information appropriate to the user. Functions can 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 codes required to perform embodiments of the present disclosure, data to be executed by the codes, and execution results of the codes.

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.

The network unit 150 in the present disclosure can be configured regardless of the communication mode, such as wired or wireless, and can be used in various communication networks such as a personal area network (PAN) and a wide area network (WAN). It can be configured. 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 the present disclosure may include any type of user terminal and/or any type of server. Accordingly, embodiments of the present disclosure may be performed by a server and/or a user device.

The processor 110 of the computing device 100 may obtain query data.

The query data is information captured by the device 200 and may include at least one of an image (eg, a first image, etc.) and/or a depth map (eg, a first depth map, etc.).

An image (eg, a first image, etc.) may be at least one image acquired through an imaging device (eg, the device 200). An image (eg, a first image, etc.) may include at least one of images in a format other than a depth map. For example, the image (e.g., the first image, etc.) may be a 2D image, a black and white image (e.g., a gray scale image, etc.), or a color image (e.g., an RGB image) that is an image in a format other than a depth map. etc.) may be included.

The depth map (e.g., the first depth map) is a three-dimensional value corresponding to each pixel present in the image captured by the device 200 and an image plane corresponding to the image captured by the device 200. It may be an image or information that shows the relative distance from each other. Accordingly, the depth map (eg, first depth map) may include information related to the distance from the location where a specific image is taken to the surface of the subject (eg, object, etc.). The depth map (eg, first depth map) may include metadata corresponding to the depth map (eg, first depth map).

Metadata may refer to additional information used to perform visual localization. For example, metadata includes information related to an imaging device (e.g., device 200, virtual device, etc.), pixel coordinate information of feature points, descriptor of feature points, image (e.g., device (e.g., device 200), the location information of the object included in the image (e.g., the image taken by the device 200, the image taken by the virtual device, etc.), the land obtained from the image (e.g., the image taken by the device 200, the image taken by the virtual device, etc.) It may include additional information other than the image, such as mark information and character information obtained from the image. For example, metadata corresponding to the first depth map includes information related to the device 200, pixel coordinate information of feature points, descriptors of feature points, and objects included in images captured by the device 200. It may include at least one of location information, landmark information obtained from an image captured by the device 200, and/or text information obtained from an image captured by the device 200.

The three-dimensional value may be a three-dimensional value corresponding to each pixel in an image (for example, an image captured by the device 200, an image captured by a virtual device, etc.).

The image plane may correspond to the screen of a device (eg, device 200, virtual device, etc.) arranged to capture an image, area, etc.

According to some embodiments of the present disclosure, the processor 110 may obtain an image in a format other than a depth map (eg, a first image, etc.) from the device 200. The processor 110 may acquire a first depth map from an image in a format other than a depth map (eg, a first image, etc.) using a pre-learned depth map acquisition model. For example, the processor 110 may obtain a first image and a first depth map corresponding to the first image.

The depth map acquisition model may be a neural network built through deep learning or machine learning. The depth map acquisition model may be a model learned to obtain a depth map from an image. For example, the depth map acquisition model may include at least one of a Deep Plane Sweep Network (DPS-Net) model and/or a DenseDepth model.

The DPS-Net model may be a model that can obtain a depth map by setting the cost volume from deep features using a plane sweep algorithm and normalizing it. The plane sweep algorithm may be an algorithm that finds the intersection between line segments in a set of line segments (eg, polygons, etc.). Specifically, the DPS-Net model is a feature extraction part of 3D points from neighboring continuous images with baselines captured by a camera, and a cost volume for multi-view images. It may be composed of a generative network, a cost aggregation part with a convolutional layer structure related to context-awareness, and a depth map regression part that infers a depth map through CNN.

A DenseDepth model can be composed of encoders and decoders. The DenseDepth model can ultimately reconstruct the depth map through a series of processes such as feature extraction, down-sampling, combining, and up-sampling of input data. The DenseDepth model can perform feature extraction and down-sampling processes on input images (e.g., RGB images, gray-scale images, etc.) through the encoder part. And the DenseDepth model can perform an up-sampling process by referring to the concatenation operation on the features extracted through the decoder part and the size of the image. In the DenseDepth model, the weights can be updated in a way that minimizes the loss value obtained through the depth map information, which is the correct answer label, and the loss function. The quality comparison between the depth map estimation data generated through the DenseDepth model and the depth map data corresponding to the correct answer label is SSIM (structural similarity index metric), PSNR (Peak Signal-to-Noise Ratio), background noise, This can be done through analysis of indicators such as profile characteristics at the object boundary. However, the depth map acquisition model is not limited to this and may include a neural network based on various depth map acquisition technologies built through deep learning or machine learning.

Additionally, the depth map acquisition model may be trained in advance using the depth map acquisition dataset. A depth map acquisition dataset may refer to a set of data for learning and verifying a depth map acquisition model. The depth map acquisition dataset may include a depth map acquisition training dataset and/or a depth map acquisition verification dataset. The depth map acquisition learning dataset may be a set of data used for learning in the learning process of the depth map acquisition model. A depth map acquisition validation dataset may be a set of data used to evaluate a depth map acquisition model.

The processor 110 may obtain first depth map feature point information from the first depth map using a pre-learned depth map feature point acquisition model.

The depth map feature point acquisition model may include a neural network built through deep learning or machine learning. The depth map feature point acquisition model can be trained in advance through deep learning or machine learning to acquire feature points from a specific depth map included in specific input data. The depth map feature point acquisition model may be learned using a depth map feature point dataset. The depth map feature point dataset may include a depth map feature point learning dataset and/or a depth map feature point verification dataset. The depth map feature point learning dataset may be a set of data used in the learning process of the depth map feature point acquisition model. The depth map feature point verification dataset may be a set of data used to evaluate a depth map feature point acquisition model, which is a model for visual localization. As another example, the depth map feature point acquisition model is a pre-trained model based on supervised learning to recognize as a feature point a point where the amount of change in at least one of the color or geometric pattern in the object included in the depth map exceeds a predetermined threshold. can correspond to .

The first depth map feature point information may be information related to a feature acquired from the first depth map input to the pre-learned depth map feature point acquisition model. For example, the first depth map feature point information may include at least one first depth map feature point and/or a first depth map descriptor. At least one first depth map feature point may be a coordinate for each feature portion of the first depth map. At least one first depth map descriptor may include information for describing each of at least one first depth map feature point. For example, the at least one first depth map descriptor may include information about the directionality and size of each of the at least one first depth map feature point and/or the relationship between surrounding pixels of each of the at least one first depth map feature point. there is.

The processor 110 may obtain depth map comparison data by comparing the first depth map feature point information with reference depth map feature point information of a pre-stored reference depth map.

Additionally, the processor 110 may match the reference depth map feature point information of each of the plurality of pre-stored reference depth maps with the first depth map feature point information. The processor 110 may determine a matching reference depth map that matches the first depth map feature point information from among a plurality of pre-stored reference depth maps. For example, the processor 110 may determine the reference depth map of the reference depth map feature point information, which has the most feature points matching the first depth map feature point information, as the matching reference depth map. Additionally, the processor 110 may obtain depth map comparison data by comparing the matching reference depth map feature point information of the matching reference depth map with the first depth map feature point information.

The reference depth map may be a pre-stored depth map used to estimate the pose of the device 200 that captured the first depth map.

Reference depth map feature point information of the reference depth map may be information related to features obtained from the reference depth map. For example, the reference depth map feature point information may include at least one reference depth map feature point and/or a reference depth map descriptor (descriptor). At least one reference depth map feature point may be a coordinate for each feature part of the reference depth map. At least one reference depth map descriptor may include information for describing each of at least one reference depth map feature point. For example, at least one reference depth map descriptor may include information about the directionality and size of each of the at least one reference depth map feature point and/or the relationship between surrounding pixels of each of the at least one reference depth map feature point.

The depth map comparison data compares the first depth map feature point information and the reference depth map feature point information of the pre-stored reference depth map, and provides information about each of at least one depth map matching feature point (or a plurality of depth map matching feature points) that matches each other. May contain information. For example, the depth map comparison data may include 2D depth map matching coordinate values and/or 3D depth map matching coordinate values of each of at least one depth map matching feature point. The 2D matching depth map coordinate value may include the two-dimensional coordinates of the depth map matching feature point (e.g., (x,y), where x and y are real numbers, etc.). For example, the 2D depth map matching coordinate values may include two-dimensional pixel coordinate values in the first depth map. The 3D depth map matching coordinate value may include the three-dimensional coordinates of the depth map matching feature point (e.g., (x, y, z), where x, y, and z are real numbers, etc.). For example, the 3D depth map matching coordinate value may include coordinate values that are three-dimensional in the coordinate system of the reference device associated with the pre-stored reference depth map.

The processor 110 may estimate the camera pose of the device 200 related to the first depth map based on the depth map comparison data. For example, the processor 110 may estimate the camera pose based on the 2D coordinate values and 3D coordinate values of each of the plurality of depth map matching feature points included in the depth map comparison data.

The camera pose may include location information and direction information of the device 200 related to the first depth map. The location information of the camera pose may include coordinates at which the device 200 exists (eg, (x,y,z), where x, y, and z are real numbers, etc.). The direction information of the camera pose may include at least one of the direction and/or angle from which the device 200 is facing.

According to another embodiment of the present disclosure, first image feature point information can be obtained from the first image using a pre-learned image feature point acquisition model.

The image feature point acquisition model may include a neural network built through deep learning or machine learning. The image feature point acquisition model can be trained in advance through deep learning or machine learning to acquire feature points from a specific image included in specific input data. The image feature point acquisition model may be learned using an image feature point dataset. The image feature point dataset may include an image feature point learning dataset and/or an image feature point verification dataset. An image feature point learning dataset may be a set of data used in the learning process of an image feature point acquisition model. The image feature point verification dataset may be a set of data used to evaluate an image feature point acquisition model, which is a model for visual localization. As another example, the image feature point acquisition model corresponds to a pre-trained model 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 patterns in an object included in the image exceeds a predetermined threshold. It can be.

The first image feature point information may be information related to a feature acquired from the first image input to the pre-learned image feature point acquisition model. For example, the first image feature point information may include at least one first image feature point and/or a first image descriptor. At least one first image feature point may be a coordinate for each characteristic part of the first image. At least one first image descriptor may include information for describing each of at least one first image feature point. For example, the at least one first image descriptor may include information about the directionality and size of each of the at least one first image feature point and/or the relationship between pixels surrounding each of the at least one first image feature point.

The processor 110 may obtain image comparison data by comparing the first image feature point information with reference image feature point information of a pre-stored reference image.

Additionally, the processor 110 may match the reference image feature point information of each of the plurality of pre-stored reference images with the first image feature point information. The processor 110 may determine a matching reference image that matches the first image feature point information from among a plurality of pre-stored reference images. For example, the processor 110 may determine the reference image of the reference image feature point information with the most feature points matching the first image feature point information as the matching reference image. Additionally, the processor 110 may obtain image comparison data by comparing the matching reference image feature point information of the matching reference image with the first image feature point information.

The reference image may be a pre-stored image used to estimate the pose of the device 200 that captured the first image.

Reference image feature point information of the reference image may be information related to features obtained from the reference image. For example, the reference image feature point information may include at least one reference image feature point and/or a reference image descriptor. At least one reference image feature point may be a coordinate for each characteristic part of the reference image. At least one reference image descriptor may include information for describing each of at least one reference image feature point. For example, at least one reference image descriptor may include information about the directionality and size of each of the at least one reference image feature point and/or the relationship between pixels surrounding each of the at least one reference image feature point.

The image comparison data may include information about each of at least one image matching feature point (or a plurality of image matching feature points) that matches each other by comparing the first image feature point information with the reference image feature point information of the pre-stored reference image. . For example, the image comparison data may include 2D image matching coordinate values and/or 3D image matching coordinate values of each of at least one image matching feature point. The 2D matching image coordinate value may include the two-dimensional coordinates of the image matching feature point (for example, (x,y), where x and y are real numbers, etc.). For example, the 2D depth map matching coordinate values may include two-dimensional pixel coordinate values in the first image. The 3D depth map matching coordinate value may include the three-dimensional coordinates of the image matching feature point (e.g., (x, y, z), where x, y, and z are real numbers, etc.). For example, the 3D depth map matching coordinate values may include coordinate values that are three-dimensional in the coordinate system of the reference device associated with the pre-stored reference image.

The processor 110 may estimate the camera pose of the device 200 related to the first image based on the image comparison data. For example, the processor 110 may estimate the camera pose based on the 2D coordinate values and 3D coordinate values of each of the plurality of image matching feature points included in the image comparison data.

The camera pose may include location information and direction information of the device 200 related to the first image. The location information of the camera pose may include coordinates at which the device 200 exists (eg, (x,y,z), where x, y, and z are real numbers, etc.). The direction information of the camera pose may include at least one of the direction and/or angle from which the device 200 is facing.

Processor 110 may create a 3D model.

For example, the processor 110 may select a space for any space scanned or photographed using an imaging device (e.g., a space associated with the first image and/or the first depth map received from the device 200). 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 3D model based on point cloud information.

A 3D model is a model 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 the image and/or depth map received from the device 200.

The processor 110 may place a virtual device corresponding to the camera pose in a pre-generated 3D model.

For example, the processor 110 may determine location information and direction information of the virtual device based on the estimated camera pose of the device 200. The virtual device is placed on the 3D model and may include a camera to photograph a specific area. The location information of the virtual device may include coordinates at which the virtual device exists (eg, coordinates on a 3D model of the virtual device). The direction information of the virtual device may include at least one of the direction and/or angle from which the virtual device is facing (eg, the direction and/or angle on the 3D model of the virtual device).

The processor 110 may place the virtual device in the 3D model based on the location information and direction information of the virtual device. When acquiring camera information of the device 200, the processor 110 sets the virtual device settings (e.g., the camera's settings) applied to the environment of the 3D model based on the camera information of the device 200. specifications, etc.) can be determined.

The camera information of the device 200 may be information about a camera for photographing a specific area. For example, camera information of the device 200 may include at least one of camera location information and/or direction information. Additionally, the camera information of the device 200 may include hardware-related information of the device 200. For example, the camera information of the device 200 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'. You can. The focal length information may be information about the distance from the main point of the device 200 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 distortion of a lens applied to the camera of the device 200. Film information may be information related to the size of the film applied to the camera of the device 200. The sensor information may be information related to a camera sensor applied to the camera of the device 200. The processor 110 may determine the aspect ratio (ratio of width to height) of an image acquired through a virtual device based on sensor or film information of the device 200. However, the camera information of the device 200 is not limited to this and may include various camera information.

The processor 110 may obtain the second depth map using a virtual device.

The second depth map is an image or information that separates the three-dimensional value corresponding to each pixel in the image taken from the virtual device and the relative distance from the image plane corresponding to the image taken from the virtual device. You can. Accordingly, the second depth map may include information related to the distance from the location where a specific image is captured to the surface of the subject (eg, object, etc.). The second depth map may include metadata corresponding to the second depth map. For example, metadata corresponding to the second depth map includes information related to the virtual device, pixel coordinate information of feature points, descriptor of feature points, location information of objects included in images taken from the virtual device, and virtual device information. It may include at least one of landmark information obtained from an image captured by a device and/or character information obtained from an image captured by a virtual device.

The processor 110 may determine an effective value of the camera pose by comparing the first depth map and the second depth map.

For example, the processor 110 may use a pixel-wise operation to obtain difference values for each first pixel from the first depth map and the second depth map.

Pixel-wise calculation may be a method of calculating difference values for each corresponding pixel between the first depth map and the second depth map.

The processor 110 may determine the number of difference values corresponding to a predetermined first standard (for example, a difference of 10 or more, etc.) among the difference values for each first pixel as an effective value of the camera pose.

As another example, the processor 110 may obtain difference values for each second pixel from the first depth map and the second depth map using a neighborhood operation.

The neighborhood operation calculates pixels corresponding to each other between the first depth map and the second depth map using pixels that exist within a predetermined range (e.g., 1 pixel, 2 pixels, etc. from the reference pixel) for each pixel of the first depth map. This may be a method of calculating the difference value. For example, the neighborhood operation selects pixels that exist within a predetermined range of the first pixel of the first depth map (e.g., the first pixel, the second pixel, the third pixel, the fourth pixel, etc.) and the second depth. Difference values between the first pixel of the map (e.g., the difference value between the first pixel of the first depth map and the first pixel of the second depth map, the second pixel of the first depth map and the first pixel of the second depth map) Difference value from 1 pixel, difference value between the 3rd pixel of the first depth map and the 1st pixel of the second depth map, difference value between the 4th pixel of the first depth map and the 1st pixel of the second depth map etc.), the minimum value (or median value, maximum value, etc.) may be determined as the difference between the first pixel of the first depth map and the first pixel of the second depth map. The neighborhood operation may obtain pixel-specific difference values between the first depth map and the second depth map by repeating the process of determining the difference between the first pixel of the first depth map and the first pixel of the second depth map.

The processor 110 may determine the number of difference values corresponding to a predetermined second standard (for example, a difference of 20 or more) among the difference values for each pixel as the effective value of the camera pose.

The first pixel-specific difference values and/or the second pixel-specific difference values may be differences in values related to positions in each pixel. However, the first pixel-specific difference values and/or the second pixel-specific difference values are not limited to this and may include differences in various information.

The processor 110 may re-estimate the camera pose when the effective value of the camera pose corresponds to a predetermined third criterion (eg, 5 or more, 10 or less, etc.).

For example, processor 110 may obtain a third depth map. The processor 110 may obtain third depth map feature point information from the third depth map using a pre-learned depth map feature point acquisition model. The processor 110 may obtain depth map comparison data by comparing the third depth map feature point information with reference depth map feature point information of a pre-stored reference depth map. The processor 110 may estimate the camera pose of the device related to the third depth map based on the depth map comparison data. The processor 110 may place a virtual device corresponding to the camera pose in a pre-generated 3D model. The processor 110 may obtain the fourth depth map using a virtual device. The processor 110 may determine an effective value of the camera pose by comparing the third depth map and the fourth depth map.

For another example, the processor 110 may obtain a second image and a fifth depth map corresponding to the second image. The processor 110 may obtain second image feature point information from the second image using a pre-learned image feature point acquisition model. The processor 110 may obtain image comparison data by comparing the second image feature point information with reference image feature point information of a pre-stored reference image. The processor 110 may estimate the camera pose of the device related to the second image based on the image comparison data. The processor 110 may place a virtual device corresponding to the camera pose in a pre-generated 3D model. The processor 110 may obtain the sixth depth map using a virtual device. The processor 110 may determine an effective value of the camera pose by comparing the fifth depth map and the sixth depth map.

Device 200 may include any type of terminal capable of interacting with a server or other computing device. Device 200 includes, for example, mobile phones, smart phones, laptop computers, personal digital assistants (PDAs), slate PCs, tablet PCs, and ultrabooks. ) may include.

Additionally, device 200 may include software and/or hardware to generate images and/or depth maps. Accordingly, device 200 may generate an image and/or depth map and transmit it to computing device 100. In one embodiment, the device 200 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 device 200 may include at least one of a camera, scanner, Lidar, and/or vision sensor. The computing device 100 may include a device (eg, device 200) or may be linked to an external device (eg, device 200) wirelessly or wired.

The network may include any wired or wireless communication network through which the computing device 100 and the device 200 can transmit and receive data and signals of any type to each other. For example, computing device 100 may receive an image and/or depth map from device 200 over a network.

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

In the present disclosure, the feature point acquisition model (eg, depth map feature point acquisition model, image feature point acquisition model, etc.) and the depth map acquisition model may correspond to an artificial intelligence-based model. In additional embodiments of the present disclosure, artificial intelligence-based models may be used to estimate the pose of a camera and/or create a character.

Throughout this disclosure, the terms artificial intelligence-based model, model, computational model, neural network, network function, and neural network may be used interchangeably. 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 teacher learning regarding data classification, the learning data may be data in which each learning 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, computational model, neural network, network function, and neural network may be used interchangeably. 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 specification, weights and parameters may be used with the same meaning.) And 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, the data structure including the weights of the neural network may include weights that are changed during the neural network learning process and/or the data structure including the weights for which neural network learning has been completed. Therefore, the above-mentioned 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.

FIG. 3 is a diagram illustrating a process for determining an effective value of a camera pose using pixel-wise calculation by a computing device according to some embodiments of the present disclosure.

Referring to FIG. 3, the processor 110 uses pixel-wise operation to create a feature map including difference values for each first pixel from the first depth map 310 and the second depth map 320. You can obtain (330). The feature map 330 may be an image containing difference values for each corresponding pixel between the first depth map 310 and the second depth map 320.

For example, the processor 110 subtracts the value of the first pixel 321 of the second depth map 320 from the value of the first pixel 311 of the first depth map 310 to obtain the value of the feature map 330. The value of the first pixel 331 can be calculated. The processor 110 calculates the difference between the value of the first pixel 311 of the previous first depth map 310 and the value of the first pixel 321 of the second depth map 320 using pixel-wise operation. This process can be repeated for all pixels of the first depth map to obtain a feature map 330 including difference values for each pixel between the first depth map and the second depth map.

FIG. 4 is a diagram illustrating a process of determining an effective value of a camera pose using a neighborhood operation by a computing device according to some embodiments of the present disclosure.

Referring to FIG. 4, the processor 110 uses a neighborhood operation to create a feature map 430 including difference values for each first pixel from the first depth map 410 and the second depth map 420. ) can be obtained. The feature map 430 may be an image containing difference values for each corresponding pixel between the first depth map 410 and the second depth map 420.

For example, the processor 110 uses pixels that exist within a predetermined range 412 for each pixel of the first depth map 410 to determine the difference between the first depth map 410 and the second depth map 420. The difference value for each corresponding pixel can be calculated. For example, the processor 110 may select pixels 412 that are within a predetermined range of the first pixel 411 of the first depth map 410 and the first pixel 421 of the second depth map 420. Among the difference values, the minimum value (or median value, maximum value, etc.) is the difference between the first pixel 411 of the first depth map 410 and the first pixel 421 of the second depth map 420. It can be determined as the first pixel 431 of the map 430. The processor 110 repeats the process of determining the difference between the first pixel 411 of the previous first depth map 410 and the first pixel 421 of the second depth map 420 to determine the first depth map 410. ) and the second depth map 420 may be obtained.

FIG. 5 is a flowchart illustrating a method for determining the effectiveness of a camera pose using visual localization according to some embodiments of the present disclosure.

The processor 110 may obtain a first depth map (S110).

The processor 110 may obtain an image in a format other than a depth map from the device 200. The processor 110 may obtain a first depth map from an image using a pre-learned depth map acquisition model.

The processor 110 may obtain first depth map feature point information from the first depth map using a pre-learned depth map feature point acquisition model (S120).

The pre-trained depth map feature point acquisition model may be trained in advance through deep learning or machine learning to acquire feature points from a specific depth map included in specific input data.

The processor 110 may obtain depth map comparison data by comparing the first depth map feature point information with the reference depth map feature point information of the pre-stored reference depth map (S130).

The processor 110 may match reference depth map feature point information and first depth map feature point information of each of the plurality of pre-stored reference depth maps.

The processor 110 may determine a matching reference depth map that matches the first depth map feature point information from among a plurality of pre-stored reference depth maps.

The processor 110 may obtain the depth map comparison data by comparing matching reference depth map feature point information and first depth map feature point information of the matching reference depth map.

The processor 110 may estimate the camera pose of the device related to the first depth map based on the depth map comparison data (S140).

The processor 110 may estimate the camera pose based on the 2D coordinate values and 3D coordinate values of each of the plurality of matching feature points included in the depth map comparison data.

The 2D coordinate value may include two-dimensional pixel coordinate values in the first depth map.

The 3D coordinate value may include three-dimensional coordinate values in the coordinate system of the reference device associated with the reference depth map.

The processor 110 may place a virtual device corresponding to the camera pose in a pre-generated 3D model (S150).

The pre-generated 3D model may be created based on spatial information about the space related to the first depth map scanned or photographed using an imaging device.

The processor 110 may obtain a second depth map using a virtual device (S160).

The processor 110 may determine an effective value of the camera pose by comparing the first depth map and the second depth map (S170).

The processor 110 may use pixel-wise operation to obtain difference values for each first pixel from the first depth map and the second depth map.

The processor 110 may determine the number of difference values corresponding to a predetermined first standard among the difference values for each first pixel as an effective value of the camera pose.

The processor 110 may obtain difference values for each second pixel from the first depth map and the second depth map using a neighborhood operation.

The processor 110 may determine the number of difference values corresponding to a predetermined second standard among the difference values for each second pixel as an effective value of the camera pose.

The processor 110 may re-estimate the camera pose when the effective value of the camera pose corresponds to a predetermined third standard.

FIG. 6 is a flowchart illustrating a method for determining the effectiveness of a camera pose using visual localization according to several other embodiments of the present disclosure.

The processor 110 may obtain a first image and a first depth map corresponding to the first image (S210).

The processor 110 may obtain the first image in a format other than a depth map from the device.

The processor 110 may acquire a first depth map from the first image using a pre-learned depth map acquisition model.

The processor 110 may acquire first image feature point information from the first image using a pre-learned image feature point acquisition model (S220).

The pre-trained image feature point acquisition model can be trained in advance through deep learning or machine learning to acquire feature points from a specific image included in specific input data.

The processor 110 may obtain image comparison data by comparing the first image feature point information with reference image feature point information of a pre-stored reference image (S230).

The processor 110 may match reference image feature point information of each of the plurality of pre-stored reference images with first image feature point information.

The processor 110 may determine a matching reference image that matches the first image feature point information from among a plurality of pre-stored reference images.

The processor 110 may obtain image comparison data by comparing matching reference image feature point information of the matching reference image with first image feature point information.

The processor 110 may estimate the camera pose of the device related to the first image based on the image comparison data (S240).

The processor 110 may estimate the camera pose based on the 2D coordinate values and 3D coordinate values of each of the plurality of matching feature points included in the image comparison data.

The 2D coordinate value may include two-dimensional pixel coordinate values in the first image.

The 3D coordinate value may include three-dimensional coordinate values in the coordinate system of the reference device associated with the reference image.

The processor 110 may place a virtual device corresponding to the camera pose in a pre-generated 3D model (S250).

A pre-generated 3D model may be created based on spatial information about a space related to a first image scanned or photographed using an imaging device.

The processor 110 may obtain a second depth map using a virtual device (S260).

The processor 110 may determine an effective value of the camera pose by comparing the first depth map and the second depth map (S270).

The processor 110 may use pixel-wise operation to obtain difference values for each first pixel from the first depth map and the second depth map.

The processor 110 may determine the number of difference values corresponding to a predetermined first standard among the difference values for each first pixel as an effective value of the camera pose.

The processor 110 may obtain difference values for each second pixel from the first depth map and the second depth map using a neighborhood operation.

The processor 110 may determine the number of difference values corresponding to a predetermined second standard among the difference values for each second pixel as an effective value of the camera pose.

The processor 110 may re-estimate the camera pose when the effective value of the camera pose corresponds to a predetermined third standard.

The steps shown in FIGS. 5 and 6 are exemplary steps. Accordingly, it will also be apparent to those skilled in the art that some of the steps in FIGS. 5 and 6 may be omitted or additional steps may be present without departing from the scope of the present disclosure. Additionally, specific details regarding the components (eg, computing device 100, device 200, etc.) depicted in FIGS. 5 and 6 may be replaced with the content previously described with reference to FIGS. 1 to 4 .

As described above with reference to FIGS. 1 to 6 , the computing device 100 may determine the effectiveness of the camera pose of the device 200 through visual localization using an image and a reference image. Additionally, the computing device 100 may determine the validity of the camera pose of the device 200 through visual localization using the depth map and the reference depth map.

The computing device 100 may re-estimate the camera pose when the camera pose becomes inaccurate (when the effective value corresponds to a predetermined condition). Accordingly, the computing device 100 can improve the accuracy of the camera pose of the device 200 by continuously estimating the camera pose of the device 200, which becomes inaccurate over time.

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 (13)

A method for visual localization performed by a computing device including a processor, comprising:
Obtaining a first depth map;
Obtaining first depth map feature point information from the first depth map using a pre-learned depth map feature point acquisition model;
Comparing the first depth map feature point information with reference depth map feature point information of a pre-stored reference depth map to obtain depth map comparison data;
estimating a camera pose of a device associated with the first depth map based on the depth map comparison data;
Placing a virtual device corresponding to the camera pose on a pre-generated 3D model;
Obtaining a second depth map using the virtual device;
Obtaining difference values for each pixel from the first depth map and the second depth map using a pixel-wise operation or a neighborhood operation; and
determining the number of difference values corresponding to a predetermined standard among the difference values for each pixel as a valid value of the camera pose;
Including,
method.
According to claim 1,
The step of acquiring the first depth map includes:
Obtaining an image in a format other than a depth map from the device; and
Obtaining a first depth map from the image using a pre-trained depth map acquisition model;
Including,
method.
According to claim 1,
The step of acquiring the depth map comparison data is,
Matching reference depth map feature point information of each of a plurality of pre-stored reference depth maps with the first depth map feature point information;
determining a matching reference depth map that matches the first depth map feature point information from among the plurality of pre-stored reference depth maps; and
Comparing matching reference depth map feature point information of the matching reference depth map and the first depth map feature point information to obtain the depth map comparison data;
Including,
method.
According to claim 1,
The step of estimating the camera pose is,
estimating the camera pose based on 2D coordinate values and 3D coordinate values of each of a plurality of matching feature points included in the depth map comparison data;
Including,
method.
According to claim 4,
The 2D coordinate values are,
Containing two-dimensional pixel coordinate values in the first depth map,
method.
According to claim 4,
The 3D coordinate values are,
Containing three-dimensional coordinate values in the coordinate system of a reference device associated with the reference depth map,
method.
According to claim 1,
The pre-generated 3D model is,
Generated based on spatial information about a space related to the first depth map scanned or photographed using an imaging device,
method.
delete delete According to claim 1,
re-estimating the camera pose when the effective value of the camera pose corresponds to a third predetermined criterion;
Containing more,
method.
According to claim 1,
The pre-trained depth map feature point acquisition model is,
Learned in advance through deep learning or machine learning to obtain feature points from a specific depth map included in specific input data.
method.
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, the steps comprising:
Obtaining a first depth map;
Obtaining first depth map feature point information from the first depth map using a pre-learned depth map feature point acquisition model;
Comparing the first depth map feature point information with reference depth map feature point information of a pre-stored reference depth map to obtain depth map comparison data;
estimating a camera pose of a device associated with the first depth map based on the depth map comparison data;
Placing a virtual device corresponding to the camera pose on a pre-generated 3D model;
Obtaining a second depth map using the virtual device;
Obtaining difference values for each pixel from the first depth map and the second depth map using a pixel-wise operation or a neighborhood operation; and
determining the number of difference values corresponding to a predetermined standard among the difference values for each pixel as a valid value of the camera pose;
Including,
A computer program stored on a computer-readable storage medium.
In a computing device for 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,
Obtain a first depth map,
Obtaining first depth map feature point information from the first depth map using a pre-learned depth map feature point acquisition model,
Obtaining depth map comparison data by comparing the first depth map feature point information with reference depth map feature point information of a pre-stored reference depth map,
Estimating a camera pose of the device associated with the first depth map based on the depth map comparison data,
Place a virtual device corresponding to the camera pose in a pre-generated 3D model,
Obtaining a second depth map using the virtual device,
Obtain difference values for each pixel from the first depth map and the second depth map using a pixel-wise operation or a neighborhood operation, and
Determining the number of difference values corresponding to a predetermined standard among the difference values for each pixel as an effective value of the camera pose,
Computing device.

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