KR102627175B1 - Method for embodying occlusion of virtual object - Google Patents

Abstract

A method for implementing occlusion of a virtual object performed by a computing device according to some embodiments of the present disclosure, the method comprising: obtaining a first depth map based on an image received from the device. step; Obtaining a reference depth map for an area corresponding to the image using a preset 3D model; and determining occlusion of the virtual object implemented in the device based on the first depth map, the reference depth map, and the position value of the virtual object, wherein the first depth map, the reference The step of determining occlusion of the virtual object implemented in the device based on the depth map and the position value of the virtual object includes pixels cut-off from at least one pixel of the first depth map. If present, obtaining a second depth map in which the value of the cutoff pixel is transformed into a predetermined value; Comparing the second depth map and the reference depth map to generate a second feature map indicating differences in values of each pixel; and determining occlusion of the virtual object implemented in the device based on the second feature map and the position value of the virtual object.

Description

{METHOD FOR EMBODYING OCCLUSION OF VIRTUAL OBJECT}

This disclosure relates to a method for implementing occlusion of virtual objects.

In recent years, machine-learning technology, including deep-learning, has received attention by showing results that exceed the performance of existing methods in analyzing various types of data such as video, voice, and text. In addition, machine learning technology is being introduced and utilized in various fields due to the scalability and flexibility inherent in the technology itself.

Among the various fields where machine learning technology can be applied, the image-related field is one of the fields that is most actively introducing machine learning technology in various detailed fields such as object recognition and object classification.

Visual localization refers to a technology that identifies the surrounding environment through a camera and determines where the camera user is currently located based on this. This visual positioning can estimate in real time the location of the user who took the image and the direction the camera is looking (camera pose) from the image. Such visual positioning can, for example, estimate the position of a virtual object and implement occlusion of the virtual object implemented in the device.

Republic of Korea Patent Publication No. 10-2012-0001113 (published on January 4, 2012)

The present disclosure has been devised in response to the above-described background technology, and seeks to provide a method for implementing occlusion of virtual objects.

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

According to some embodiments of the present disclosure for solving the problems described above, a method for implementing occlusion of a virtual object performed by a computing device, the method comprising: an image received from the device; obtaining a first depth map based on; Obtaining a reference depth map for an area corresponding to the image using a preset 3D model; and determining occlusion of the virtual object implemented in the device based on the first depth map, the reference depth map, and the position value of the virtual object.

Alternatively, the step of obtaining the first depth map may include, when the first depth map is included in the image, obtaining the first depth map included in the image.

Alternatively, the step of acquiring the first depth map may include, when the image includes a specific image in a format other than the first depth map, using a pre-learned depth map acquisition model to obtain the first depth map from the specific image. It may include obtaining the first depth map.

Alternatively, the preset 3D model may be created in advance based on spatial information about the space associated with the image previously scanned or photographed using an imaging device.

Alternatively, obtaining the reference depth map for an area corresponding to the image using the preset 3D model may include receiving camera information of the device from the device; determining location information and direction information of a virtual camera based on the camera information of the device and the image; Placing the virtual camera in the preset 3D model based on location information and direction information of the virtual camera; and obtaining the reference depth map for the area corresponding to the image using the virtual camera.

Alternatively, determining occlusion of the virtual object implemented in the device based on the first depth map, the reference depth map, and the position value of the virtual object includes: Based on a relative camera pose between a depth map camera of the device and a specific image camera of the device, the reference depth map is aligned so that a scene in the first depth map corresponds to a scene in the reference depth map. Obtaining an aligned reference depth map; and determining occlusion of the virtual object implemented in the device based on the first depth map, the aligned reference depth map, and the position value of the virtual object.

Alternatively, determining occlusion of the virtual object implemented in the device based on the first depth map, the reference depth map, and the position value of the virtual object comprises: the first depth map and the Comparing the reference depth map to generate a first feature map showing differences in values of each pixel; and determining occlusion of the virtual object implemented in the device based on the first feature map and the position value of the virtual object.

Alternatively, determining occlusion of the virtual object implemented in the device based on the first depth map, the reference depth map, and the position value of the virtual object may comprise: at least one of the first depth map When a cut-off pixel exists in one pixel, obtaining a second depth map in which the value of the cut-off pixel is transformed into a predetermined value; Comparing the second depth map and the reference depth map to generate a second feature map indicating differences in values of each pixel; and determining occlusion of the virtual object implemented in the device based on the second feature map and the position value of the virtual object.

Alternatively, determining occlusion of the virtual object implemented in the device based on the first depth map, the reference depth map, and the position value of the virtual object may include: determining the size of the first depth map; If the scale and size of the reference depth map do not correspond, generating a third depth map by adjusting the first depth map to correspond to the reference depth map; Comparing the third depth map and the reference depth map to generate a third feature map indicating differences in values of each pixel; and determining occlusion of the virtual object implemented in the device based on the third feature map and the position value of the virtual object.

Alternatively, when a cut-off pixel exists in at least one pixel of the third depth map, obtaining a fourth depth map in which the value of the cut-off pixel is transformed into a predetermined value; Comparing the fourth depth map and the reference depth map to generate a fourth feature map indicating differences in values of each pixel; and determining occlusion of the virtual object implemented in the device based on the fourth feature map and the position value of the virtual object.

Alternatively, a computer program stored on a computer-readable storage medium, the computer program comprising instructions for causing a processor of a computing device to implement occlusion of a virtual object to perform the following steps, These include: obtaining a first depth map based on an image received from a device; Obtaining a reference depth map for an area corresponding to the image using a preset 3D model; and determining occlusion of the virtual object implemented in the device based on the first depth map, the reference depth map, and the position value of the virtual object.

Alternatively, a computing device for implementing occlusion of a virtual object, comprising: a processor; a memory storing a computer program executable by the processor; and a network unit, wherein the processor acquires a first depth map based on an image received from the device, and uses a preset 3D model to create a reference depth map for the area corresponding to the image. may be obtained, and occlusion of the virtual object implemented in the device may be determined based on the first depth map, the reference depth map, and the position value of the virtual object.

The present disclosure can implement occlusion of virtual objects.

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 is a diagram illustrating a system for implementing occlusion of a virtual object according to some embodiments of the present disclosure.
Figure 2 is a schematic diagram showing network functions according to some embodiments of the present disclosure.
3 and 4 are diagrams illustrating examples of a method for implementing occlusion of a virtual object performed on a computing device according to some embodiments of the present disclosure.
5 and 6 are flowcharts showing a method of implementing occlusion of a virtual object performed in a computing device according to some 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 “a case containing only A,” “a case containing only B,” and “a case of combining 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.

In this disclosure, network function, neural network, and neural network may be used interchangeably.

1 is a diagram illustrating a system for implementing occlusion of a virtual object according to some embodiments of the present disclosure.

Referring to FIG. 1, a system for implementing occlusion of a virtual object according to some embodiments of the present disclosure may include a computing device 100, a device 200, and a network. However, the configuration of the system shown in Figure 1 is only a simplified example. In some embodiments of the present disclosure, the system may include different configurations, and only some of the disclosed configurations may constitute the system.

The computing device 100 may include a processor 110, a memory 130, and a network unit 150. The configuration of the computing device 100 shown in FIG. 1 is only a simplified example. In one embodiment of the present disclosure, the computing device 100 may include different configurations for performing the computing environment of the computing device 100, and only some of the disclosed configurations may configure the computing device 100.

The processor 110 may be composed of one or more cores, and may include a central processing unit (CPU), a general purpose graphics processing unit (GPGPU), and a tensor processing unit (TPU) of a computing device. unit) may include a processor for data analysis and deep learning. The processor 110 may read a computer program stored in the memory 130 and perform data processing for machine learning 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, the processors of a plurality of computing devices can be used together to process learning of network functions and data classification using network functions. Additionally, a computer program executed in the computing device 100 according to an embodiment of the present disclosure may be a CPU, GPGPU, or TPU executable program.

According to one embodiment of the present disclosure, the memory 130 may store any type of data or information generated or determined by the processor 110 and any type of information received by the network unit 150. For example, the memory 130 may store and manage information such as images, depth maps, 3D models, reference images, and virtual objects received from the device 200.

According to an embodiment of the present disclosure, the memory 130 is a flash memory type, hard disk type, multimedia card micro type, or card type memory (e.g. (e.g. SD or -Only Memory), and may include at least one type of storage medium among 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 merely an example, and the present disclosure is not limited thereto.

The network unit 150 according to an embodiment of the present disclosure may include any wired or wireless communication network capable of transmitting and receiving arbitrary types of data and signals, etc., as expressed in the present disclosure. For example, the processor 110 of the computing device 100 may receive image-related data, information, signals, etc. from the device 200 through the network unit 150.

The techniques described herein can be used in the networks mentioned above, as well as other networks.

The computing device 100 according to some embodiments of the present disclosure may determine occlusion of a virtual object using a depth map.

A depth map (e.g., a first depth map, a second depth map, etc.) corresponds to a three-dimensional value and an image corresponding to each pixel present in an image (e.g., an image taken by the device 200, etc.) It may be an image or information expressed by distinguishing the relative distance from the image plane. Accordingly, the 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, an object, etc.). The depth map may include metadata corresponding to the depth map.

Metadata may refer to additional information used to perform visual localization. For example, metadata includes information related to a photographing device (e.g., device 200, virtual camera, etc.), pixel coordinate information of feature points, descriptor of feature points, and image (e.g., device 200). Location information of objects included in an image (e.g., an image captured by the device 200, an image captured by a virtual camera, etc.), and landmark information obtained from an image (e.g., an image captured by the device 200, an image captured by a virtual camera, etc.) , may include additional information other than the image, such as text information obtained from the image.

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).

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

Occlusion may mean a state in which one object is not visible because at least part of it is obscured by another object. Accordingly, occlusion of a virtual object may mean a state in which the virtual object is not visible because at least part of it is obscured by another object.

The processor 110 of the computing device 100 may obtain a first depth map based on the image received from the device 200.

The image may be at least one image acquired through an imaging device. The image may include at least one depth map and/or at least one of various images excluding the depth map. Various images other than the depth map may include, for example, 2D images, black and white images, color images, etc.

If the image received from the device 200 includes a depth map, the processor 110 may determine the corresponding depth map as the first depth map. Accordingly, when the image includes a first depth map, the processor 110 may obtain the first depth map included in the image.

The first depth map may be an image or information representing a three-dimensional value corresponding to each pixel present in an image captured by the device 200 and a relative distance from the image plane corresponding to the image. The first depth map may include first metadata corresponding to the first depth map. For example, the first depth map may be mapped to the first metadata corresponding to the first depth map.

First metadata may refer to additional information used in performing visual localization. For example, the first metadata includes information related to the device 200, location information of objects included in the first depth map, pixel coordinate information of feature points of the first depth map, and descriptors of feature points of the first depth map. ), may include additional information other than the first depth map, such as landmark information obtained from the first depth map, text information obtained from the first depth map, etc.

When the image received from the device 200 includes a specific image in a format other than a depth map (e.g., a first depth map), the processor 110 uses a pre-learned depth map acquisition model to obtain the specific image. The first depth map can be obtained from.

The depth map acquisition model may be a model that acquires a depth map from various images excluding the depth map or a pre-trained model. The depth map acquisition model may include a neural network built through deep learning or machine learning. 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., 2D images, black-and-white images, color 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 can be trained in advance using a dataset. A dataset may refer to a set of data for learning and verifying a depth map acquisition model. The dataset may include a training dataset and/or a validation dataset. A learning dataset may be a set of data used for learning in the learning process of a depth map acquisition model. A validation dataset may be a set of data used to evaluate a depth map acquisition model. Accordingly, the processor 110 may pre-train the depth map acquisition model using the dataset.

Processor 110 may create a 3D model. Specifically, the processor 110 may acquire spatial information about any space scanned or photographed using an imaging device (eg, a space related to an image received from the device 200).

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). 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 received from the device 200.

The processor 110 may use a preset 3D model to obtain a reference depth map for the area corresponding to the image.

As described above, a preset 3D model may be created in advance based on spatial information about a space related to an image previously scanned or photographed using an imaging device.

The processor 110 may receive camera information of the device 200 from the device 200. The device 200 may include a depth map camera that captures a depth map and/or a specific image camera that captures various specific images other than the depth map.

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 camera 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.

Additionally, the camera information of the device 200 may include depth map camera information of the device 200 and/or specific image camera information of the device 200.

The depth map camera information of the device 200 may include location information and direction information of the depth map camera determined based on a predetermined reference point. The predetermined reference point may be the origin or a point corresponding to the location of the reference object. For example, the predetermined reference point may be a point corresponding to the location of the depth map camera. If the predetermined reference point is a point corresponding to the location of the depth map camera, the location information of the depth map camera may be (0, 0, 0). The location information of the depth map camera may include coordinates where the depth map camera exists. The direction information of the depth map camera may include at least one of the direction and/or angle that the depth map camera is looking at.

The specific image camera information of the device 200 may include location information and direction information of the specific image camera determined based on a predetermined reference point. The location information of a specific image camera may include coordinates where the specific image camera exists. The direction information of a specific image camera may include at least one of the direction and/or angle that the specific image camera is looking at.

The processor 110 may determine the location information and direction information of the virtual camera based on the camera information of the device 200 and the image received from the device 200. The location information of the virtual camera may include coordinates where the virtual camera exists. The direction information of the virtual camera may include at least one of the direction and/or angle the virtual camera is looking at.

The processor 110 may place the virtual camera in a preset 3D model based on the location information and direction information of the virtual camera.

The processor 110 may determine virtual camera settings (eg, camera specifications, etc.) applied to the 3D model environment based on camera information of the device 200.

The processor 110 may obtain a reference depth map for an area corresponding to the image received from the device 200 using a virtual camera.

The reference depth map may be an image or information representing a three-dimensional value corresponding to each pixel in an image captured by a virtual camera and a relative distance from the image plane corresponding to the image captured by the camera. The reference depth map may include second metadata corresponding to the reference depth map. For example, the reference depth map may be mapped with second metadata corresponding to the reference depth map.

Second metadata may refer to additional information used in performing visual localization. For example, the second metadata includes information related to the virtual camera, location information of objects included in the reference depth map, pixel coordinate information of feature points of the reference depth map, descriptors of feature points of the reference depth map, and reference depth map. It may include additional information other than the reference depth map, such as landmark information obtained from, character information obtained from the reference depth map, etc.

The processor 110 may determine the occlusion of the virtual object implemented in the device 200 based on the first depth map, the reference depth map, and the position value of the virtual object. For example, a virtual object may be implemented on a 3D model and/or device 200 in response to a user's input. The processor 110 may determine material information of the virtual object in a preset manner. The preset method may include a method of arbitrarily determining among a plurality of material information, a method of setting the material information of a real object corresponding to a virtual object, etc. The virtual object may be randomly selected and implemented on the 3D model and/or device 200, for example.

When the processor 110 acquires the first depth map from a specific image in a format other than the first depth map using a pre-learned depth map acquisition model, the processor 110 obtains the first depth map from the device 200. Only information about a specific image, not information about , can be received through the network unit 150. When the processor 110 places a virtual camera in a 3D model and obtains a reference depth map based on information about a specific image, the processor 110 places the virtual camera at a location corresponding to the location of the specific image camera of the device 200. A reference depth map can be obtained. Therefore, based on the first depth map acquired using a depth map acquisition model pre-learned with a depth map taken at the location of the depth map camera and information about the specific image, the camera is placed at a location corresponding to the location of the specific image camera. The reference depth map obtained from the virtual camera may have different scenes for corresponding areas due to the positions of the different cameras. Therefore, for accurate comparison, it is necessary to align the first depth map and the reference depth map and determine the occlusion of the virtual object.

For example, the processor 110 may generate a scene of the first depth map based on the relative camera pose between the depth map camera of the device 200 and the specific image camera of the device 200 included in the camera information of the device 200. An aligned reference depth map can be obtained by aligning the reference depth map so that the scene corresponds to the scene in the reference depth map.

A relative camera pose may include relative position information and direction information between two cameras. Here, the location information may include relative coordinates between the two cameras. The direction information may include at least one of the relative direction and/or angle between the two cameras.

The processor 110 may determine a relative camera pose based on depth map camera information of the device 200 and specific image camera information of the device 200. The processor 110 may obtain an aligned reference depth map in which the reference depth maps are aligned so that the scenes in the first depth map correspond to the scenes in the reference depth map based on the determined relative camera pose.

The processor 110 may determine the occlusion of the virtual object implemented in the device 200 based on the first depth map, the aligned reference depth map, and the position value of the virtual object.

The processor 110 may compare the first depth map and the reference depth map to generate a first feature map indicating differences in values of each pixel.

The first feature map may compare pixels of the first depth map with pixels of the reference depth map corresponding to the pixels of the first depth map, and display a difference in the value of each pixel. The first feature map may display only pixel values that exceed a predetermined threshold among pixels. The predetermined threshold may be determined in advance by the user. The predetermined threshold may be an average value of the difference between the values of each pixel obtained by comparing the pixels of the first depth map and the pixels of the reference depth map corresponding to the pixels of the first depth map.

The processor 110 may determine the occlusion of the virtual object implemented in the device 200 based on the first feature map and the position value of the virtual object.

For example, the processor 110 may generate a virtual value corresponding to each of the pixels exceeding a predetermined threshold among the pixels of the first feature map and each of the pixels exceeding a predetermined threshold among the pixels of the first feature map. You can match pixels of objects. For each of the matched pixels, the processor 110 may determine occlusion of the virtual object based on the position value of the corresponding pixel of the first depth map and the position value of the corresponding pixel of the virtual object. For example, if the position value of the first pixel of the first depth map in the matched first pixel is greater (or smaller) than the position value of the first pixel of the virtual object, the processor 110 An object (e.g., a first pixel value of a virtual object) may be implemented (e.g., rendered, etc.) (or not implemented). For example, when the position value of the first pixel of the first depth map in the matched first pixel is smaller than the position value of the first pixel of the virtual object, the processor 110 receives the first pixel of the first depth map from the device 200. It may determine that the pixel is closer than the first pixel of the virtual object and not implement the virtual object at the first pixel. The processor 110 may determine occlusion of the virtual object in each of the pixels that do not exceed a predetermined threshold based on the position value of the reference depth map (or first depth map) and the position value of the virtual object.

For another example, if there are no pixels exceeding a predetermined threshold among the pixels of the first feature maps, the processor 110 may use the position value of the reference depth map (or first depth map) and the position of the virtual object. Based on the value, occlusion of the virtual object can be determined.

If a cut-off pixel exists in at least one pixel of the first depth map, the processor 110 may obtain a second depth map in which the value of the cut-off pixel is transformed into a predetermined value.

For example, the predetermined value may be an average value of the values of at least one surrounding pixel existing around the cutoff pixel. At least one neighboring pixel may be a pixel that exists within a predetermined distance (eg, 1 cm) from the cutoff pixel. At least one neighboring pixel may be a pixel that is at least partially in contact with a cutoff pixel. Accordingly, when a plurality of cut-off pixels exist, the processor 110 may determine the value of each of the plurality of cut-off pixels to be a different predetermined value. The predetermined value may be another example, a value determined by the user. The predetermined value may be, for example, an average value of the values of all pixels.

The processor 110 may compare the second depth map and the reference depth map to generate a second feature map in which differences in values of each pixel are displayed.

The processor 110 may determine the occlusion of the virtual object implemented in the device 200 based on the second feature map and the position value of the virtual object.

For example, the processor 110 may generate a virtual value corresponding to each of the pixels exceeding a predetermined threshold among the pixels of the second feature map and each of the pixels exceeding a predetermined threshold among the pixels of the second feature map. You can match pixels of objects. For each of the matched pixels, the processor 110 may determine occlusion of the virtual object based on the position value of the corresponding pixel of the second depth map and the position value of the corresponding pixel of the virtual object. For example, if the position value of the first pixel of the second depth map in the matched first pixel is greater (or smaller) than the position value of the first pixel of the virtual object, the processor 110 determines the virtual object in the first pixel. An object (e.g., a first pixel value of a virtual object) may be implemented (e.g., rendered, etc.) (or not implemented). The processor 110 may determine occlusion of the virtual object in each of the pixels that do not exceed a predetermined threshold based on the position value of the reference depth map (or second depth map) and the position value of the virtual object.

For another example, when there are no pixels exceeding a predetermined threshold among the pixels of the second feature maps, the processor 110 determines the position value of the reference depth map (or second depth map) and the position of the virtual object. Based on the value, occlusion of the virtual object can be determined.

If the scale of the first depth map does not correspond to the size of the reference depth map, the processor 110 may generate a third depth map by adjusting the first depth map to correspond to the reference depth map.

The processor 110 may compare the third depth map and the reference depth map to generate a third feature map in which differences in values of each pixel are displayed.

The processor 110 may determine the occlusion of the virtual object implemented in the device 200 based on the third feature map and the position value of the virtual object.

For example, the processor 110 may generate a virtual image corresponding to each of the pixels exceeding a predetermined threshold among the pixels of the third feature map and each of the pixels exceeding a predetermined threshold among the pixels of the third feature map. You can match pixels of objects. For each of the matched pixels, the processor 110 may determine occlusion of the virtual object based on the position value of the corresponding pixel of the third depth map and the position value of the corresponding pixel of the virtual object. For example, the processor 110 determines a virtual An object (e.g., a first pixel value of a virtual object) may be implemented (e.g., rendered, etc.) (or not implemented). The processor 110 may determine occlusion of the virtual object in each of the pixels that do not exceed a predetermined threshold based on the position value of the reference depth map (or third depth map) and the position value of the virtual object.

For another example, when there are no pixels exceeding a predetermined threshold among the pixels of the third feature maps, the processor 110 determines the position value of the reference depth map (or third depth map) and the position of the virtual object. Based on the value, occlusion of the virtual object can be determined.

If a cut-off pixel exists in at least one pixel of the third depth map, the processor 110 may obtain a fourth depth map in which the value of the cut-off pixel is transformed into a predetermined value.

The processor 110 may compare the fourth depth map and the reference depth map to generate a fourth feature map indicating differences in values of each pixel.

The processor 110 may determine the occlusion of the virtual object implemented in the device 200 based on the fourth feature map and the position value of the virtual object.

For example, the processor 110 may generate a virtual value corresponding to each of the pixels exceeding a predetermined threshold among the pixels of the fourth feature map and each of the pixels exceeding a predetermined threshold among the pixels of the fourth feature map. You can match pixels of objects. The processor 110 may determine the occlusion of the virtual object based on the position value of the corresponding pixel of the fourth depth map and the position value of the corresponding pixel of the virtual object for each of the matched pixels. For example, if the position value of the first pixel of the fourth depth map in the matched first pixel is greater (or smaller) than the position value of the first pixel of the virtual object, the processor 110 An object (e.g., a first pixel value of a virtual object) may be implemented (e.g., rendered, etc.) (or not implemented). The processor 110 may determine occlusion of the virtual object in pixels that do not exceed a predetermined threshold based on the position value of the reference depth map (or fourth depth map) and the position value of the virtual object.

For another example, if there are no pixels exceeding a predetermined threshold among the pixels of the fourth feature maps, the processor 110 may use the position value of the reference depth map (or fourth depth map) and the position of the virtual object. Based on the value, occlusion of the virtual object can be determined.

The processor 110 may transmit information about the virtual object to the device 200 through the network unit 150 to implement occlusion of the virtual object in the device 200.

Device 200 may refer to any type of nodes in the system that have a mechanism for communication with computing device 100. For example, the device 200 may include a mobile terminal, a smart phone, etc. Additionally, the device 200 may include a depth map and/or modules for generating various images other than a depth map. For example, the device 200 may include a depth map camera for capturing a depth map and/or a specific image camera for capturing various images other than the depth map. Accordingly, the device 200 may generate the first depth map and/or a specific image in a format other than the first depth map and transmit it to the computing device 100. Additionally, the user of the device 200 may recognize the current location and/or direction based on the device location and direction information received from the computing device 100. Additionally, the device 200 may implement occlusion of the virtual object based on information about the virtual object received from the computing device 100. For example, the device 200 may implement occlusion of the virtual object in augmented reality, virtual reality, etc. based on information about the virtual object received from the computing device 100. However, the method of implementing the virtual object is not limited to this, and the processor 110 may implement the virtual object on the device 200 in various ways.

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 from device 200 through a network. The device 200 may receive information about a virtual object from the computing device 100 through a network.

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

Throughout this specification, 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 teacher learning, learning data in which the correct answer is labeled in each learning data is used (i.e., labeled learning data), and in the case of non-teacher 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 non-teachable learning for 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.

According to an embodiment of the present disclosure, a computer-readable medium storing a data structure 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.

3 and 4 are diagrams illustrating examples of a method for implementing occlusion of a virtual object performed on a computing device according to some embodiments of the present disclosure.

FIG. 3(a) may be an image including the object 20 captured by the device 200. FIG. 3(b) may be an image including the first virtual object 30 implemented in the device 200. FIG. 3(c) may be an image in which occlusion of the first virtual object 30 is implemented in the device 200.

3(a) to 3(c), the processor 110 of the computing device 100 selects the object 20 at the pixel 10 where the object 20 and the first virtual object 30 match. The pixel position value of and the pixel position value of the first virtual object 30 may be compared. The processor 110 compares the position value of the pixel of the object 20 in the pixel 10 with the position value of the pixel of the first virtual object 30 in the pixel 10, and as a result, the object in the pixel 10 ( The position value of the pixel of 20 is smaller than the position value of the pixel of the first virtual object 30 in pixel 10 (that is, the position of the object 20 is closer to the device than the position of the first virtual object 30). 200) can be determined as the closer one. Therefore, as shown in FIG. 3(c), the processor 110 may not render the first virtual object 30 in the pixel 10. The processor 110 may transmit information about the first virtual object 30 to the device 200 to implement occlusion of the first virtual object 30 in the device 200 as shown in FIG. 3(c).

FIG. 4(a) may be an image including the object 20 captured by the device 200. FIG. 4(b) may be an image including the second virtual object 40 implemented in the device 200. FIG. 4(c) may be an image in which occlusion of the second virtual object 40 is implemented in the device 200.

4(a) to 4(c), the processor 110 of the computing device 100 selects the object 20 at the pixel 10 where the object 20 and the second virtual object 40 match. The pixel position value of and the pixel position value of the second virtual object 40 may be compared. The processor 110 compares the position value of the pixel of the object 20 in the pixel 10 with the position value of the pixel of the second virtual object 40 in the pixel 10, and as a result, the object in the pixel 10 ( The position value of the pixel of 20 is greater than the position value of the pixel of the second virtual object 40 in pixel 10 (that is, the position of the object 20 is greater than the position of the second virtual object 40 in the device ( 200) can be determined. Accordingly, as shown in FIG. 4(c), the processor 110 may render the second virtual object 40 in the pixel 10. The processor 110 may transmit information about the second virtual object 40 to the device 200 to implement occlusion of the second virtual object 40 in the device 200 as shown in FIG. 4(c).

5 and 6 are flowcharts showing a method of implementing occlusion of a virtual object performed in a computing device according to some embodiments of the present disclosure.

Referring to FIG. 5 , the processor 110 of the computing device 100 may obtain a first depth map based on the image received from the device 200 (S110).

When the image includes a first depth map, the processor 110 may obtain the first depth map included in the image.

If the image includes a specific image in a format other than the first depth map, the processor 110 may obtain the first depth map from the specific image using a pre-trained depth map acquisition model.

The processor 110 may use a preset 3D model to obtain a reference depth map for the area corresponding to the image (S120).

A 3D model may be created in advance based on spatial information about a space related to an image previously scanned or photographed using an imaging device.

The processor 110 may determine the occlusion of the virtual object implemented in the device based on the first depth map, the reference depth map, and the position value of the virtual object (S130).

For example, the processor 110 may compare the first depth map and the reference depth map to generate a first feature map indicating differences in values of each pixel. The processor 110 may determine the occlusion of the virtual object implemented in the device based on the first feature map and the position value of the virtual object.

For another example, if a cutoff pixel exists in at least one pixel of the first depth map, the processor 110 may obtain a second depth map in which the value of the cutoff pixel is transformed into a predetermined value. The processor 110 may compare the second depth map and the reference depth map to generate a second feature map in which differences in values of each pixel are displayed. The processor 110 may determine the occlusion of the virtual object implemented in the device 200 based on the second feature map and the position value of the virtual object.

For another example, when the size (scale) of the first depth map and the size of the reference depth map do not correspond, the processor 110 creates a third depth map in which the first depth map is adjusted to correspond to the reference depth map. can be created. The processor 110 may compare the third depth map and the reference depth map to generate a third feature map indicating differences in values of each pixel. The processor 110 may determine the occlusion of the virtual object implemented in the device based on the third feature map and the position value of the virtual object. If a cutoff pixel exists in at least one pixel of the third depth map, the processor 110 may obtain a fourth depth map obtained by transforming the value of the cutoff pixel into a predetermined value. By comparing the fourth depth map and the reference depth map, a fourth feature map in which differences in values of each pixel are displayed can be generated. The processor 110 may determine the occlusion of the virtual object implemented in the device based on the fourth feature map and the position value of the virtual object.

Referring to FIG. 6, the processor 110 may receive camera information of the device 200 from the device 200 (S210).

The processor 110 may determine the location information and direction information of the virtual camera based on the camera information and image of the device 200 (S220).

The processor 110 may place the virtual camera in a preset 3D model based on the location information and direction information of the virtual camera (S230).

The processor 110 may obtain a reference depth map for an area corresponding to the image using a virtual camera (S240).

The processor 110 causes the scene in the first depth map to correspond to the scene in the reference depth map based on the relative camera pose between the depth map camera of the device and the specific image camera of the device 200 included in the camera information of the device 200. A sorted reference depth map can be obtained by sorting the reference depth map.

The processor 110 may determine the occlusion of the virtual object implemented in the device based on the first depth map, the aligned reference depth map, and the position value of the virtual object.

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 uses a depth map obtained based on an image received from the device 200, a reference depth map obtained using a 3D model, and a depth map for a virtual object. By determining the occlusion of the virtual object implemented in the device 200 based on the information, more accurate virtual object occlusion can be implemented than when virtual object occlusion is implemented only with information about a pre-stored map. Specifically, in the past, occlusion of virtual objects was implemented only with information about the map stored in advance, so that occlusion of virtual objects was not smooth and was implemented awkwardly due to no consideration of objects added in real time in the real map that changes in real time. It has been done. However, the computing device 100 according to the present invention can implement occlusion of the virtual object by additionally using images received in real time from the device 200. Accordingly, in the device 200, the virtual object can be accurately implemented according to the actual situation without any awkward loss of color.

Additionally, in the past, when applying a virtual object based on an image received from a device, it took a long time to analyze the image received from the device and apply the virtual object, and a lot of resources were required. However, the computing device 100 according to the present invention can reduce the time and resources required to analyze images and apply virtual objects by using the reference depth map.

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 (8)

A method for implementing occlusion of a virtual object performed by a computing device, the method comprising:
Obtaining a first depth map based on an image received from a device;
Obtaining a reference depth map for an area corresponding to the image using a preset 3D model; and
determining occlusion of the virtual object implemented in the device based on the first depth map, the reference depth map, and the position value of the virtual object;
Including,
Determining occlusion of the virtual object implemented in the device based on the first depth map, the reference depth map, and the position value of the virtual object includes:
When a cut-off pixel exists in at least one pixel of the first depth map, obtaining a second depth map in which the value of the cut-off pixel is transformed into a predetermined value;
Comparing the second depth map and the reference depth map to generate a second feature map indicating differences in values of each pixel; and
determining occlusion of the virtual object implemented in the device based on the second feature map and the position value of the virtual object;
Including,
method.
According to claim 1,
The step of acquiring the first depth map includes:
When the image includes the first depth map, obtaining the first depth map included in the image;
Including,
method.
According to claim 1,
The preset 3D model is,
Generated in advance based on spatial information about the space related to the image scanned or photographed in advance using an imaging device,
method.
According to claim 1,
Determining occlusion of the virtual object implemented in the device based on the first depth map, the reference depth map, and the position value of the virtual object includes:
Comparing the first depth map and the reference depth map to generate a first feature map indicating differences in values of each pixel; and
determining occlusion of the virtual object implemented in the device based on the first feature map and the position value of the virtual object;
Including,
method.
According to claim 1,
Determining occlusion of the virtual object implemented in the device based on the first depth map, the reference depth map, and the position value of the virtual object includes:
If the size (scale) of the first depth map does not correspond to the size of the reference depth map, generating a third depth map by adjusting the first depth map to correspond to the reference depth map;
Comparing the third depth map and the reference depth map to generate a third feature map indicating differences in values of each pixel; and
determining occlusion of the virtual object implemented in the device based on the third feature map and the position value of the virtual object;
Including,
method.
According to claim 5,
When a cut-off pixel exists in at least one pixel of the third depth map, obtaining a fourth depth map obtained by transforming the value of the cut-off pixel into a predetermined value;
Comparing the fourth depth map and the reference depth map to generate a fourth feature map indicating differences in values of each pixel; and
determining occlusion of the virtual object implemented in the device based on the fourth feature map and the position value of the virtual object;
Containing more,
method.
A computer program stored on a computer-readable storage medium, the computer program comprising instructions for causing a processor of a computing device to implement occlusion of a virtual object to perform the following steps:
Obtaining a first depth map based on an image received from a device;
Obtaining a reference depth map for an area corresponding to the image using a preset 3D model; and
determining occlusion of the virtual object implemented in the device based on the first depth map, the reference depth map, and the position value of the virtual object;
Including,
Determining occlusion of the virtual object implemented in the device based on the first depth map, the reference depth map, and the position value of the virtual object includes:
When a cut-off pixel exists in at least one pixel of the first depth map, obtaining a second depth map in which the value of the cut-off pixel is transformed into a predetermined value;
Comparing the second depth map and the reference depth map to generate a second feature map indicating differences in values of each pixel; and
determining occlusion of the virtual object implemented in the device based on the second feature map and the position value of the virtual object;
Including,
A computer program stored on a computer-readable storage medium.
In a computing device for implementing occlusion of a virtual object,
processor;
a memory storing a computer program executable by the processor; and
network department;
Including,
The processor,
Obtain a first depth map based on the image received from the device,
Using a preset 3D model, obtain a reference depth map for the area corresponding to the image, and
Determine occlusion of the virtual object implemented in the device based on the first depth map, the reference depth map, and the position value of the virtual object,
In the process of determining occlusion of the virtual object implemented in the device based on the first depth map, the reference depth map, and the position value of the virtual object,
If a cut-off pixel exists in at least one pixel of the first depth map, obtain a second depth map in which the value of the cut-off pixel is transformed into a predetermined value,
Compare the second depth map and the reference depth map to generate a second feature map indicating the difference in values of each pixel, and
Determining occlusion of the virtual object implemented in the device based on the second feature map and the position value of the virtual object,
Computing device.

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