MOTION-AWARE NEURAL RADIANCE FIELD NETWORK TRAINING

A method includes obtaining multiple training image frames of a scene, where the training image frames are captured at multiple viewpoints and multiple viewing angles relative to the scene. The method also includes generating multiple initial motion maps using the training image frames and identifying three-dimensional (3D) feature points associated with the scene using the training image frames. The method further includes generating tuned motion masks using the initial motion maps and projections of the 3D feature points onto the initial motion maps. In addition, the method includes training a machine learning model using the training image frames and the tuned motion masks, where the machine learning model is trained to generate 3D information about the scene from viewpoints and viewing angles not captured in the training image frames.

TECHNICAL FIELD

This disclosure relates generally to imaging systems. More specifically, this disclosure relates to motion-aware neural radiance field network training.

BACKGROUND

A neural radiance field (NeRF) network is an example of a multi-layer perceptron (MLP) network, which is a fully-connected multi-layer neural network. A NeRF network can be used to generate novel views of a complex three-dimensional (3D) scene based on a partial set of two-dimensional (2D) images. In other words, the NeRF network can be trained using 2D images of a 3D scene so that additional images of the 3D scene can be generated from specific viewpoints and specific viewing directions, including viewpoints and viewing directions not captured in the 2D images. For example, a NeRF network can be trained to generate color and density information within a scene, and classical volume rendering equations can be used to convert the color and density information into an image of the scene from a specific viewpoint and a specific viewing direction.

SUMMARY

This disclosure relates to motion-aware neural radiance field network training.

In a first embodiment, a method includes obtaining multiple training image frames of a scene, where the training image frames are captured at multiple viewpoints and multiple viewing angles relative to the scene. The method also includes generating multiple initial motion maps using the training image frames and identifying three-dimensional (3D) feature points associated with the scene using the training image frames. The method further includes generating tuned motion masks using the initial motion maps and projections of the 3D feature points onto the initial motion maps. In addition, the method includes training a machine learning model using the training image frames and the tuned motion masks, where the machine learning model is trained to generate 3D information about the scene from viewpoints and viewing angles not captured in the training image frames. In another embodiment, a non-transitory machine readable medium includes instructions that when executed cause at least one processor to perform the method of the first embodiment.

In a second embodiment, an electronic device includes at least one processing device configured to obtain multiple training image frames of a scene, where the training image frames are captured at multiple viewpoints and multiple viewing angles relative to the scene. The at least one processing device is also configured to generate multiple initial motion maps using the training image frames and identify 3D feature points associated with the scene using the training image frames. The at least one processing device is further configured to generate tuned motion masks using the initial motion maps and projections of the 3D feature points onto the initial motion maps. In addition, the at least one processing device is configured to train a machine learning model using the training image frames and the tuned motion masks, where the machine learning model is trained to generate 3D information about the scene from viewpoints and viewing angles not captured in the training image frames.

In a third embodiment, a method includes identifying a specified viewpoint and a specified viewing angle associated with a scene. The method also includes providing the specified viewpoint and the specified viewing angle to a machine learning model. The method further includes generating 3D information about the scene using the machine learning model. In addition, the method includes rendering an image of the scene from the specified viewpoint and at the specified viewing angle using the 3D information about the scene. The machine learning model is trained to compensate for motion within the scene that is captured in training image frames used to train the machine learning model. In another embodiment, an apparatus includes at least one processing device configured to perform the method of the third embodiment. In still another embodiment, a non-transitory machine readable medium includes instructions that when executed cause at least one processor to perform the method of the third embodiment.

DETAILED DESCRIPTION

FIGS.1through9, discussed below, and the various embodiments of this disclosure are described with reference to the accompanying drawings. However, it should be appreciated that this disclosure is not limited to these embodiments and all changes and/or equivalents or replacements thereto also belong to the scope of this disclosure.

As discussed above, a neural radiance field (NeRF) network is an example of a multi-layer perceptron (MLP) network, which is a fully-connected multi-layer neural network. A NeRF network can be used to generate novel views of a complex three-dimensional (3D) scene based on a partial set of two-dimensional (2D) images. In other words, the NeRF network can be trained using 2D images of a 3D scene so that additional images of the 3D scene can be generated from specific viewpoints and specific viewing directions, including viewpoints and viewing directions not captured in the 2D images. For example, a NeRF network can be trained to generate color and density information within a scene, and classical volume rendering equations can be used to convert the color and density information into an image of the scene from a specific viewpoint and a specific viewing direction.

Training data that is used to train a NeRF network typically includes multiple training images captured of a scene, where the training images capture the scene from different viewpoints and directions. However, the training images need to capture a static scene, meaning a scene with no motion, which can be difficult or impossible to achieve in a number of situations. To try and overcome this obstacle, non-rigid neural radiance field (NR-NeRF) networks have been developed. A NR-NeRF network attempts to disentangle a dynamic scene into a canonical neural radiance field (which captures a scene's static geometry and appearance) and a scene deformation field (which defines how the canonical neural radiance field is deformed to create each individual training image). Scene deformation can be implemented using a ray-bending network, which allows straight rays to be deformed non-rigidly. For example, in an NR-NeRF network, the ray-bending network can be trained to transform various points' positions on rays into their canonical configurations using rigidity and deformation field values.

Unfortunately, both standard NeRF networks and NR-NeRF networks are generally unable to handle motion within scenes very well. As a result, motion in scenes can introduce severe artifacts in the images produced using the NeRF and NR-NeRF networks. As examples, halo artifacts are often created around transition points between light and dark areas of scenes, and ghosting artifacts are often created due to object motion that occurs while an image capturing process occurs.

This disclosure provides various techniques related to motion-aware neural radiance field network training. As described in more detail below, the disclosed techniques use motion estimation, such as motion estimation generated during multi-frame processing (MFP), to augment the training of a machine learning model (such as an NR-NeRF network) so that one or more moving objects in a scene can be rendered properly. For example, multiple training image frames of a scene can be obtained, where the training image frames can be captured at multiple viewpoints and multiple viewing angles relative to the scene. Initial motion maps associated with the training image frames can be generated, and sparse 3D feature points associated with the scene (such as stationary points within the scene) can be estimated using the training image frames. Tuned motion masks can be generated using the initial motion maps and projections of the 3D feature points onto the initial motion maps. In some cases, this may allow a motion map to be generated for each non-reference image frame contained in a collection of training image frames during multi-frame processing and fine-tuned using the projected sparse 3D feature points to produce a tuned motion mask. The tuned motion masks can be used to guide training of a machine learning model, such as an NR-NeRF network. For instance, the tuned motion masks can help to guide the training of the machine learning model by differentiating between motion areas and non-motion areas within the training image frames. In this way, the disclosed techniques enable “motion-aware” training of NR-NeRF networks or other machine learning models.

A NR-NeRF network or other machine learning model trained in this manner may be used or deployed to one or more consumer electronic devices or other devices for use. For example, a specified viewpoint and a specified viewing angle associated with the scene can be identified and provided as inputs to the trained machine learning model. The machine learning model can be used to generate 3D information about the scene, such as color and density information. An image of the scene from the specified viewpoint and at the specified viewing angle can be rendered using the 3D information about the scene. Here, the machine learning model has been trained to compensate for motion within the scene, where that motion is captured in training image frames used to train the machine learning model. In this way, the disclosed techniques can be used to facilitate the generation of views of 3D scenes, even if there is motion within the training image frames used to train the machine learning model.

Note that while some of the embodiments discussed below are described in the context of use in consumer electronic devices (such as smartphones), this is merely one example. It will be understood that the principles of this disclosure may be implemented in any number of other suitable contexts and may use any suitable device or devices. Also note that while some of the embodiments discussed below are described based on the assumption that one device (such as a server) performs motion-aware training of a NR-NeRF network or other machine learning model that is deployed to one or more other devices (such as one or more consumer electronic devices), this is also merely one example. It will be understood that the principles of this disclosure may be implemented using any number of devices, including a single device that both trains and uses a machine learning model. In general, this disclosure is not limited to use with any specific type(s) of device(s).

FIG.1illustrates an example network configuration100including an electronic device according to this disclosure. The embodiment of the network configuration100shown inFIG.1is for illustration only. Other embodiments of the network configuration100could be used without departing from the scope of this disclosure.

According to embodiments of this disclosure, an electronic device101is included in the network configuration100. The electronic device101can include at least one of a bus110, a processor120, a memory130, an input/output (I/O) interface150, a display160, a communication interface170, or a sensor180. In some embodiments, the electronic device101may exclude at least one of these components or may add at least one other component. The bus110includes a circuit for connecting the components120-180with one another and for transferring communications (such as control messages and/or data) between the components.

The processor120includes one or more processing devices, such as one or more microprocessors, microcontrollers, digital signal processors (DSPs), application specific integrated circuits (ASICs), or field programmable gate arrays (FPGAs). In some embodiments, the processor120includes one or more of a central processing unit (CPU), an application processor (AP), a communication processor (CP), or a graphics processor unit (GPU). The processor120is able to perform control on at least one of the other components of the electronic device101and/or perform an operation or data processing relating to communication or other functions. As described in more detail below, the processor120may perform various operations related to motion-aware training and/or use of a NR-NeRF network or other machine learning model.

The memory130can include a volatile and/or non-volatile memory. For example, the memory130can store commands or data related to at least one other component of the electronic device101. According to embodiments of this disclosure, the memory130can store software and/or a program140. The program140includes, for example, a kernel141, middleware143, an application programming interface (API)145, and/or an application program (or “application”)147. At least a portion of the kernel141, middleware143, or API145may be denoted an operating system (OS).

The kernel141can control or manage system resources (such as the bus110, processor120, or memory130) used to perform operations or functions implemented in other programs (such as the middleware143, API145, or application147). The kernel141provides an interface that allows the middleware143, the API145, or the application147to access the individual components of the electronic device101to control or manage the system resources. The application147may support various functions related to motion-aware training and/or use of a NR-NeRF network or other machine learning model. These functions can be performed by a single application or by multiple applications that each carry out one or more of these functions. The middleware143can function as a relay to allow the API145or the application147to communicate data with the kernel141, for instance. A plurality of applications147can be provided. The middleware143is able to control work requests received from the applications147, such as by allocating the priority of using the system resources of the electronic device101(like the bus110, the processor120, or the memory130) to at least one of the plurality of applications147. The API145is an interface allowing the application147to control functions provided from the kernel141or the middleware143. For example, the API145includes at least one interface or function (such as a command) for filing control, window control, image processing, or text control.

The I/O interface150serves as an interface that can, for example, transfer commands or data input from a user or other external devices to other component(s) of the electronic device101. The I/O interface150can also output commands or data received from other component(s) of the electronic device101to the user or the other external device.

The display160includes, for example, a liquid crystal display (LCD), a light emitting diode (LED) display, an organic light emitting diode (OLED) display, a quantum-dot light emitting diode (QLED) display, a microelectromechanical systems (MEMS) display, or an electronic paper display. The display160can also be a depth-aware display, such as a multi-focal display. The display160is able to display, for example, various contents (such as text, images, videos, icons, or symbols) to the user. The display160can include a touchscreen and may receive, for example, a touch, gesture, proximity, or hovering input using an electronic pen or a body portion of the user.

The communication interface170, for example, is able to set up communication between the electronic device101and an external electronic device (such as a first electronic device102, a second electronic device104, or a server106). For example, the communication interface170can be connected with a network162or164through wireless or wired communication to communicate with the external electronic device. The communication interface170can be a wired or wireless transceiver or any other component for transmitting and receiving signals.

The electronic device101further includes one or more sensors180that can meter a physical quantity or detect an activation state of the electronic device101and convert metered or detected information into an electrical signal. For example, one or more sensors180include one or more cameras or other imaging sensors for capturing images of scenes. The sensor(s)180can also include one or more buttons for touch input, a gesture sensor, a gyroscope or gyro sensor, an air pressure sensor, a magnetic sensor or magnetometer, an acceleration sensor or accelerometer, a grip sensor, a proximity sensor, a color sensor (such as a red green blue (RGB) sensor), a bio-physical sensor, a temperature sensor, a humidity sensor, an illumination sensor, an ultraviolet (UV) sensor, an electromyography (EMG) sensor, an electroencephalogram (EEG) sensor, an electrocardiogram (ECG) sensor, an infrared (IR) sensor, an ultrasound sensor, an iris sensor, or a fingerprint sensor. The sensor(s)180can further include an inertial measurement unit, which can include one or more accelerometers, gyroscopes, and other components. In addition, the sensor(s)180can include a control circuit for controlling at least one of the sensors included here. Any of these sensor(s)180can be located within the electronic device101.

The first external electronic device102or the second external electronic device104can be a wearable device or an electronic device-mountable wearable device (such as an HMD). When the electronic device101is mounted in the electronic device102(such as the HMD), the electronic device101can communicate with the electronic device102through the communication interface170. The electronic device101can be directly connected with the electronic device102to communicate with the electronic device102without involving with a separate network. The electronic device101can also be an augmented reality wearable device, such as eyeglasses, that include one or more imaging sensors.

The first and second external electronic devices102and104and the server106each can be a device of the same or a different type from the electronic device101. According to certain embodiments of this disclosure, the server106includes a group of one or more servers. Also, according to certain embodiments of this disclosure, all or some of the operations executed on the electronic device101can be executed on another or multiple other electronic devices (such as the electronic devices102and104or server106). Further, according to certain embodiments of this disclosure, when the electronic device101should perform some function or service automatically or at a request, the electronic device101, instead of executing the function or service on its own or additionally, can request another device (such as electronic devices102and104or server106) to perform at least some functions associated therewith. The other electronic device (such as electronic devices102and104or server106) is able to execute the requested functions or additional functions and transfer a result of the execution to the electronic device101. The electronic device101can provide a requested function or service by processing the received result as it is or additionally. To that end, a cloud computing, distributed computing, or client-server computing technique may be used, for example. WhileFIG.1shows that the electronic device101includes the communication interface170to communicate with the external electronic device104or server106via the network162or164, the electronic device101may be independently operated without a separate communication function according to some embodiments of this disclosure.

The server106can include the same or similar components110-180as the electronic device101(or a suitable subset thereof). The server106can support to drive the electronic device101by performing at least one of operations (or functions) implemented on the electronic device101. For example, the server106can include a processing module or processor that may support the processor120implemented in the electronic device101. As described in more detail below, the server106may perform various operations related to motion-aware training and/or use of a NR-NeRF network or other machine learning model.

FIG.2illustrates an example NR-NeRF network200according to this disclosure. For ease of explanation, the NR-NeRF network200is described as being trained by the server106and used by the electronic device101in the network configuration100ofFIG.1. However, the NR-NeRF network200may be trained and used by the same component (such as the server106) or trained and used by any other suitable device(s) and in any other suitable system(s).

As shown inFIG.2, the NR-NeRF network200includes a ray deformation network202and a NeRF network204. The ray deformation network202generally operates to bend rays206associated with a viewer perspective or other perspective208of an observation frame210. The observation frame210can represent or include a 3D scene that is captured using 2D images. The rays206represent rays of light in the observation frame210that may be viewed at the perspective208. The ray deformation network202accomplishes this by identifying points212along the rays206, where each of the points212is associated with corresponding coordinates214within the observation frame210. The ray deformation network202operates to convert the coordinates214of the points212into converted coordinates216, which are associated with points218positioned along bent rays220within a canonical frame210′. In other words, the ray deformation network202is trained to transform x=(x, y, z) (which are the coordinates214of each point212on a ray206) into its canonical configuration x′=(x′, y′, z′)=(x, y, z)+m×(δx, δy, δz). Here, m represents a learned rigidity score, and (δx, δy, δz) represents a deformation field. As described above, the NR-NeRF network200attempts to disentangle a dynamic scene into a canonical neural radiance field (which captures a scene's static geometry and appearance) and a scene deformation field (which defines how the canonical neural radiance field is deformed to create each individual training image), where scene deformation can be implemented using a ray-bending network. This transformation may be expressed as ψ(x)→{δx, m}. As described below, after the transformation, color and density information can be estimated using the NeRF network204, and this estimation can be expressed as(x+ψ(x))→(c, σ). Here, ψ(x) represents a network-induced ray-deformation operation, and(x) represents the operation of the NeRF network204. Also, c represents the color information generated by the NeRF network204, and σ represents the density information generated by the NeRF network204.

In this example, the ray deformation network202receives, identifies, or otherwise obtains the coordinates214for the points212along the rays206. Each set of coordinates214is provided to a combiner222, which combines the coordinates214with a latent deformation code224(denoted ω). The latent deformation code224is used to define a deformation field. The resulting outputs of the combiner222are provided to a deformation field (DF) multi-layer perceptron (MLP) network226, which warps 3D points of the observation frame210into 3D points of the canonical frame210′ based on the latent deformation code224. The canonical frame210′ represents a frame of reference of a canonical model. The resulting outputs of the combiner222are also provided to a rigidity network228, which segments the scene within the observation frame210into rigid and non-rigid portions, such as a rigid background and a non-rigid foreground. Essentially, a scene captured in multiple image frames can be represented by the canonical model, and deformations (motion) within the scene can be expressed as deformations. The deformations can be defined using per-time-step warpings of the canonical model, and these deformations are provided by the ray deformation network202. The rigidity network228helps to force the identification of the warpings to be limited to non-rigid portions of the scene while assuming that the rigid portions of the scene do not deform. This permits reconstruction of the rigid portions of the scene without deformation. The combiner222and the deformation field MLP network226here collectively form at least part of a non-rigid ray-bending network.

The NeRF network204in this example is implemented using a NeRF MLP network230. The NeRF MLP network230generally operates to process information, including information generated by the ray deformation network202, in order to generate 3D information associated with the scene. In this example, the 3D information includes color data232and density data234. The color data232and the density data234can collectively define the color and density of each point within a 3D space represented by the canonical model. The color data232and the density data234may be used in any suitable manner. In this example, the color data232and the density data234are provided to a rendering function236, which can use classical volume rendering equations or other techniques to render one or more output images238. For instance, based on a specified viewpoint and a specified viewing angle (which can collectively define an image plane), the rendering function236can render an image238of the scene from that viewpoint and at that viewing angle using the color data232and the density data234.

As discussed below, the NR-NeRF network200is trained using training image frames that capture motion within the scene. Among other things, the training image frames are used to generate motion masks, which can segment the training image frames into regions with motion and regions without motion or without significant motion. This helps to guide the training of the NR-NeRF network200by allowing differentiation between motion areas and non-motion areas within the training image frames. For example, within non-motion areas, the NR-NeRF network200can have less or no freedom to perform ray-bending. Within motion areas, the NR-NeRF network200can have more freedom to perform ray-bending. This helps to train the NR-NeRF network200more effectively, which allows the NR-NeRF network200to generate the color and density data232and234more effectively and render moving objects more accurately.

AlthoughFIG.2illustrates one example of a NR-NeRF network200, various changes may be made toFIG.2. For example, other embodiments of NR-NeRF networks or other machine learning models may be used here. The specific implementation of the NR-NeRF network200as shown inFIG.2is for illustration and explanation only and can vary depending on the implementation.

FIG.3illustrates an example architecture300supporting motion-aware NR-NeRF network training according to this disclosure. For ease of explanation, the architecture300is described as being used by the server106in the network configuration100ofFIG.1to train the NR-NeRF network200ofFIG.2. However, the architecture300could be used by any other suitable device(s) and in any other suitable system(s), and the architecture300may be used to train any other suitable machine learning model(s).

As shown inFIG.3, the architecture300includes or is used in conjunction with an imaging sensor array302, which represents one or more cameras or other imaging sensors used to capture training image frames304of a scene. The imaging sensor array302includes any suitable number of cameras or other imaging sensors. In some cases, the imaging sensor array302may include multiple cameras or other imaging sensors that are used to capture training image frames304of a scene at or near the same time from different locations. In other cases, the imaging sensor array302may include one or more cameras or other imaging sensors that are used to capture training image frames304of a scene at different times from different locations, such as when the one or more imaging sensors are located on a portable device. In general, the imaging sensor array302may include one or more cameras or other imaging sensors configured to capture training image frames304of a scene. Each training image frame304can have any suitable resolution and format, such as raw/Bayer image frames, RGB image frames, or other image frames. The collection of training image frames304associated with the scene can capture some type of movement within the scene, such as one or more natural or manmade objects that are moving.

The training image frames304are provided to a structure from motion determination function306. The structure from motion determination function306generally operates to estimate sparse 3D feature points within the scene using the 2D training image frames304associated with the scene. The phrase “structure from motion” here refers to the fact that the sparse 3D feature points within the scene can be estimated using training image frames304that are captured at different locations. By capturing the training image frames304at different locations, motion parallax can be used to derive depth information of the scene. Motion parallax refers to the fact that objects move differently (such as by different amounts) when viewed from different locations depending on their depths from those locations. The structure from motion determination function306processes the training image frames304associated with the scene in order to generate outputs308, which can include an identification of sparse 3D feature points that represent stationary points within the scene and an identification of the poses of the imaging sensors used to capture the training image frames304associated with the scene. Note that this can be performed for any suitable number of training image frames304associated with the scene.

The training image frames304and the outputs308are provided to a motion mask generation function310. The motion mask generation function310generally operates to produce motion masks312, each of which identifies areas of motion within one or more of the training image frames304. For example, the motion mask generation function310may implement a multi-frame processing (MFP) operation, such as one or more of the processes described in U.S. Pat. No. 10,805,649 (which is hereby incorporated by reference in its entirety). This patent describes processes for blending image frames, and those processes can involve the generation of motion maps. The motion maps generated in accordance with this patent can be modified as discussed below to generate the motion masks312. For instance, at least some of the motion maps initially generated for the training image frames304can be fine-tuned by projecting the sparse 3D feature points from the outputs308onto the motion maps. Note, however, that the motion maps may be generated in any other suitable manner and may or may not be generated as part of a multi-frame processing operation.

A motion-aware NR-NeRF training function314uses the training image frames304and the motion masks312to train a machine learning model, such as the NR-NeRF network200. For example, the motion-aware NR-NeRF training function314may use the motion masks312to guide the training of the NR-NeRF network200or other machine learning model by differentiating motion and non-motion areas within the training image frames304. As a particular example, the NR-NeRF network200during training can have less or no freedom to perform ray-bending within non-motion areas and more freedom to perform ray-bending within motion areas. The motion-aware NR-NeRF training function314can train the NR-NeRF network200or other machine learning model using the training image frames304and the motion masks312, ideally until a loss associated with the machine learning model falls below a threshold loss value. The loss identifies differences or errors between actual and desired outputs from the machine learning model. When the loss falls below the threshold loss value, that is indicative that the NR-NeRF network200or other machine learning model has been trained to properly generate 3D information (such as the color data232and the density data234), at least to within a desired degree of accuracy as defined by the threshold loss value.

In some embodiments, the motion-aware NR-NeRF training function314operates as follows. For notational simplicity, assume that the ray deformation network202is denoted as ψ(xr), which describes how much a ray206should bend in order to generate a corresponding ray220. Here, xr∈X represents all points212on ray r. Also, let(·) represent the NeRF network204, where inputs to the NeRF network204include the coordinates216associated with the bent rays220and outputs of the NeRF network204include the color and density data232,234. Thus, the outputs of the NeRF network204can be expressed as {cr, σr}=(xr+ψ(xr)), where crrepresents the color data232(such as RGB data) and σrrepresents the density data234. The final color Ĉi∈Ĉ for each pixel i in a rendered image of the scene (with R representing a volume rendering weighting operation performed by the rendering function236) can be express as Ĉi=ΣrR(cr, σr).

For a specific sample k on a ray r, R can be defined as R(ck, σk)=Tk(1−exp(−σkδk))ck, where Tk=exp(−Σj=1k-1σjδj) and δkrepresents the distance between two samples. Since the motion masks312can be used to differentiate between areas of the training image frames304with and without motion, the NR-NeRF network200can be trained to correctly generate 3D information for both motion and non-motion areas. In some cases, this can involve the use of a loss function that includes different terms associated with the motion and non-motion areas. For example, it is possible to classify each ray206as a motion ray or a static ray depending on whether that ray206passes through a motion region or a non-motion region of a training image frame304as defined by the associated motion mask312. All of the points212associated with motion rays206can be referred to collectively as motion points xmotion, and all of the points212associated with static rays206can be referred to collectively as static points xstatic(where xmotion∪xstatic=X).

Given this, one possible loss function may be defined as follows.

Here, the term Lallrepresents an overall loss value calculated for the machine learning model being trained and which can be compared to the threshold loss value. The term Ldata(C, Ĉ) represents a data loss calculated for the machine learning model being trained. The term Ldivergenceψrepresents a divergence loss calculated for the machine learning model being trained, and the value y represents a weight applied to the divergence loss. The terms Loffsetψ(xmotion) and Loffsetψ(xstatic) represent offset losses respectively associated with motion and static rays, and the values λmotionand λstaticrepresent weights respectively associated with the motion and static rays. By making λmotion«λstatic, the motion masks312can be used to artificially guide rays206to have more flexibility to bend in motion areas and less or no flexibility to bend in non-motion areas.

In some cases, the data loss Ldata(C, Ĉ) may be defined as follows.

Here, Ĉ represents the rendered color as generated by the rendering function236using the color data232generated by the NR-NeRF network200, and C represents a ground truth color (meaning the expected color to be generated. Also, in some cases, the divergence loss may be defined as follows.

Here, the divergence div may be defined as

where Tr(·) represents the trace operator.

In addition, in some cases, each of the offset losses Loffsetψ(xmotion) and Loffsetψ(xstatic) may be defined as follows. In the description above, the ray deformation network202was denoted as ψ(xr), and the output of the NeRF network204was expressed as {cr, σr}=(xr+ψ(xr)). Now, as part of a more detailed loss explanation, the exact expression for ψ(xr) may be defined as ψ(xr)=mr⊙δxr, where mrand δxrrespectively represent a rigidity score (generated by the rigidity network228) and a bending amount (generated by the deformation field MLP network226) for ray samples. Also, ⊙ represents an element-wise multiplication (Hadamard product). Given this, the output generated by the NeRF network204can be defined as {cr, σr}=(xr+mr⊙δxr). Based on that, each of the offset losses Loffsetψ(xmotion) and Loffsetψ(xstatic) may be defined as follows.

The same equation may be used separately to determine the motion and static offset losses Loffsetψ(xmotion) and Loffsetψ(xstatic). Here, αr is defined for a specific sample k on a ray r as αk=Tkσk, where Tk=exp(−Σj=1k-1σjδj). Also, δkrepresents the distance between adjacent samples, and η represents a regularization parameter. Note that the exponent component in Equation (4) provides two desirable properties. For non-rigid regions (meaning mris closer to 1), the offset loss becomes anloss. As a result, the gradient is independent of the magnitude of the offset, so small and large offsets motions are treated equally (unlike with an2loss). Also, relative to an2loss, this approach encourages sparsity in the offsets field. For rigid regions (meaning mris closer to 0), the offset loss becomes an2loss, which tapers off in its gradient magnitude as the offset magnitude approaches zero and prevents noisy gradients that an1loss has for tiny offsets of rigid regions.

Once the motion-aware NR-NeRF training function314has been used to train the NR-NeRF network200or other machine learning model, the trained machine learning model may be placed into use, deployed to one or more other devices for use, or used in any other suitable manner. For example, the trained machine learning model may be used during inferencing operations to generate color data232and density data234that is provided to the rendering function236, which can generate images238of the scene from specified viewpoints and viewing angles.

AlthoughFIG.3illustrates one example of an architecture300supporting motion-aware NR-NeRF network training, various changes may be made toFIG.3. For example, various components or functions shown inFIG.3may be combined, further subdivided, rearranged, replicated, or omitted and additional components can be added according to particular needs.

FIG.4illustrates an example structure from motion determination function306in the architecture300ofFIG.3according to this disclosure. As shown inFIG.4, the training image frames304are processed using a correspondence search function402. The correspondence search function402generally operates to identify overlap between the training image frames304and to identify projections of common points within the overlapping portions of the training image frames304. In this example, the correspondence search function402includes a feature extraction function404, a feature matching function406, and a geometric verification function408.

The feature extraction function404operates to identify features within the training image frames304, such as features associated with people, vehicles, buildings, natural landmarks, or other contents of the training image frames304. The feature matching function406operates to match the identified features in different training image frames304so that common features captured in multiple training image frames304are identified as being the same features. For example, the feature matching function406may determine that one or more identified features associated with the same object in different training image frames304represent the same features. The geometric verification function408attempts to verify whether the matching features are correct, such as by determining whether a valid mapping or transformation from one training image frame304to another training image frame304can be identified. The outputs of the correspondence search function402may take the form of a scene graph in which the training image frames304are nodes of the scene graph and verified pairs of training image frames304are edges of the scene graph.

The scene graph or other outputs of the correspondence search function402are provided to an incremental reconstruction function410, which generally operates to identify imaging sensor poses for the verified training image frames304and scene structure based on the verified training image frames304. In this example, the incremental reconstruction function410includes an initialization function412, an image registration function414, a triangulation function416, a bundle adjustment function418, and an outlier filtering function420.

The initialization function412operates to create an initial model of the scene based on two or more of the training image frames304. The image registration function414operates to register additional training image frames304(which were not used to create the initial model) with the initial model of the scene. Each additional training image frame304can include at least some of the scene points that are already within the model of the scene, and the triangulation function416uses the registered training image frames304to identify additional scene points within the model of the scene. Since the image registration function414and the triangulation function416are separate processes, uncertainties may exist with the imaging sensor poses and the scene points, and the bundle adjustment function418can apply nonlinear refinement to the imaging sensor poses and the scene points in order to refine the model of the scene. The outlier filtering function420reduces weights on or removes outliers in the model of the scene.

The final model of the scene generated by the incremental reconstruction function410can represent a reconstruction422of that scene, where the reconstruction422of the scene represents a 3D representation of the scene. Thus, the reconstruction422is a 3D representation of the scene and is generated based on 2D image frames of the scene. The reconstruction422may include the outputs308described above, such as stationary points within the scene and poses of the imaging sensors used to capture the training image frames304.

The model of the scene generated here can have any suitable form. In some embodiments, imaging sensor poses that are defined by the model of the scene may include an N×3×5 matrix, where N represents the number of training image frames304. Each training image frame304can be associated with two depth values that identify the closest and farthest scene content from a specific point of view associated with the corresponding pose. Also, each 3×5 matrix may include a 3×4 camera-to-world affine transform that is concatenated with a 3×1 column. The 3×1 column may include an image height, an image width, and a focal length associated with the corresponding training image frame304.

AlthoughFIG.4illustrates one example of a structure from motion determination function306in the architecture300ofFIG.3, various changes may be made toFIG.4. For example, various components or functions shown inFIG.4may be combined, further subdivided, rearranged, replicated, or omitted and additional components can be added according to particular needs.

FIG.5illustrates an example process500for motion-aware NR-NeRF network training according to this disclosure. For ease of explanation, the process500is described as being used by the server106in the network configuration100ofFIG.1to train the NR-NeRF network200ofFIG.2based on the architecture300ofFIG.3. However, the process500could be used by any other suitable device(s) with any other suitable architecture(s) and in any other suitable system(s), and the process500may be used to train any other suitable machine learning model(s).

As shown inFIG.5, the training image frames304are processed using the structure from motion determination function306. The outputs308generated by the motion determination function306include a point cloud308′ of sparse 3D points that identify stationary points within the specific scene captured by the training image frames304. Note that the positions of the cameras or other imaging sensors within the point cloud308′ are also shown here. The training image frames304are also processed using a multi-frame processing function502, which as noted above may be performed by the motion mask generation function310. The multi-frame processing function502can produce (among other things) initial motion maps504associated with at least some of the training image frames304. For example, in some cases, the multi-frame processing function502may, for a collection of training image frames304being processed, select a reference frame (with all other training image frames304being non-reference frames) and generate an initial motion map504for each non-reference frame. Each initial motion map504may identify differences between the reference frame and the corresponding non-reference frame, where those differences may be attributable to motion. A thresholding function506applies at least one threshold to the values of each initial motion map504in order to reduce or remove false indications of motion from the initial motion maps504, thereby generating initial motion masks.

A projection function508is performed to project the point cloud308′ (which identifies the stationary points within the specific scene captured by the training image frames304) onto each of the initial motion masks. Essentially, the stationary points within the point cloud308′ are overlaid onto the initial motion masks. This can be accomplished, for example, using transformations that map the 3D space of the point cloud308′ onto the 2D spaces of the initial motion masks. This results in the generation of projection motion masks510, which include both (i) the areas of motion and non-motion as identified by the multi-frame processing function502and (ii) the stationary points of the scene as identified in the point cloud308′ overlaid onto the areas of motion and non-motion. As described below, the stationary points within the point cloud308′ may be projected onto multiple versions of each initial motion mask, so the projection function508may result in the generation of multiple projection motion masks510corresponding to each training image frame304or each of a subset of the training image frames304.

A selection and combination function512generally operates to select a subset of the projection motion masks510for each of multiple training image frames304and to combine the selected projection motion masks510in order to generate a final motion mask312for that training image frame304. For example, when a training image frame304is associated with multiple projection motion masks510, the selection and combination function512may select two or more of the projection motion masks510having the least amount of identified motion overlapped with the projected stationary points. Since the projected stationary points are by definition stationary and not moving within the scene, it is not expected for large amounts of motion to be present in locations where there are more projected stationary points. Thus, the selection and combination function512can identify the projection motion masks510having the smallest amount(s) of overlap between the projected stationary points and the identified motion areas. The selection and combination function512can combine those projection motion masks510with one another in order to produce the final motion mask312for that training image frame304.

The final motion masks312generated by the motion mask generation function310are provided to the motion-aware NR-NeRF training function314, which uses the final motion masks312during motion-aware training of the NR-NeRF network200. For example, as can be seen inFIG.5, each motion mask312can be used to limit bending of rays so that (i) rays514passing through motion regions as defined by the motion mask312have more flexibility to bend and (ii) rays516passing through non-motion regions as defined by the motion mask312have less or no flexibility to bend.

AlthoughFIG.5illustrates one example of a process500for motion-aware NR-NeRF network training, various changes may be made toFIG.5. For example, various components or functions shown inFIG.5may be combined, further subdivided, rearranged, replicated, or omitted and additional components can be added according to particular needs.

FIG.6illustrates an example motion mask generation function310in the architecture300ofFIG.3according to this disclosure. InFIG.6, generation of the final motion masks312by the motion mask generation function310ofFIG.5is shown in greater detail. As shown inFIG.6, for each of N initial motion maps504produced for N training image frames304, the motion mask generation function310applies the thresholding function506in order to generate an initial motion mask602for the training image frame304. The initial motion mask602identifies any motion areas in the initial motion map504that exceed at least one threshold.

The initial motion mask602undergoes a segmentation function604to generate one or more sectionalized masks606. Each sectionalized mask606includes a different collection of connected pixels from the initial motion mask602identifying motion. For example, if each initial motion mask602includes black pixels identifying no motion and white pixels identifying motion, each sectionalized mask606can include a single collection of connected white pixels (meaning all white pixels in each sectionalized mask606form a single blob or “segment”). Also, different sectionalized masks606include different segments of pixels. Because of this, the number of sectionalized masks606generated here can vary based on the locations of any motion identified by the initial motion mask602. Note that the segmentation function604may not be needed or may produce no sectionalized masks606or one sectionalized mask606if the initial motion mask602identifies no motion or includes a single segment of pixels identifying motion.

The projection function508projects the stationary points of the scene as defined by the point cloud308′ onto each of the sectionalized masks606. This results in the generation of one or more projection motion masks510, where each projection motion mask510corresponds to one of the sectionalized masks606. The selection and combination function512can select two or more of the projection motion masks510and combine the selected projection motion masks510with one another to produce the final motion mask312for the corresponding training image frame304. For instance, the selected projection motion masks510may include the projection motion masks510having the smallest amount(s) of overlap between identified motion areas and projected stationary points from the scene. As can be seen inFIG.6, the final motion mask312generated by the selection and combination function512is cleaner and more distinctly identifies areas where the corresponding training image frame304likely contains motion. As noted above, this can be done for each of N training image frames304. The resulting collection of final motion masks312can be used (along with the associated training image frames304) to support motion-aware training of the NR-NeRF network200or other machine learning model.

AlthoughFIG.6illustrates one example of a motion mask generation function310in the architecture300ofFIG.3, various changes may be made toFIG.6. For example, various components or functions shown inFIG.6may be combined, further subdivided, rearranged, replicated, or omitted and additional components can be added according to particular needs.

It should be noted that the functions shown in or described with respect toFIGS.2through6can be implemented in an electronic device101,102,104, server106, or other device(s) in any suitable manner. For example, in some embodiments, at least some of the functions shown in or described with respect toFIGS.2through6can be implemented or supported using one or more software applications or other software instructions that are executed by the processor120of the electronic device101,102,104, server106, or other device(s). In other embodiments, at least some of the functions shown in or described with respect toFIGS.2through6can be implemented or supported using dedicated hardware components. In general, the functions shown in or described with respect toFIGS.2through6can be performed using any suitable hardware or any suitable combination of hardware and software/firmware instructions. Also, the functions shown in or described with respect toFIGS.2through6can be performed by a single device or by multiple devices.

FIGS.7A through7Cillustrate example results obtained using motion-aware NR-NeRF network training according to this disclosure. In this example,FIG.7Aillustrates a ground truth image700and a blown-up portion702of the ground truth image700. The ground truth image700captures a scene in which a toy train is moving along a track in front of and around various objects, including square or rectangular tiles behind the toy train.

FIG.7Billustrates a generated image704and a blown-up portion706of the generated image704, where the image704is generated using a standard NR-NeRF network. As can be seen by comparingFIGS.7A and7B, the generated image704is of generally low quality. Among other things, the toy train in the generated image704is very blurry, and an artifact has been created at the back portion of the toy train where a clip is located. The tiles behind the toy train in the generated image704also include various artifacts, some of which cause several tiles to not appear square or rectangular.

FIG.7Cillustrates a generated image708and a blown-up portion710of the generated image708, where the image708is generated using a NR-NeRF network200trained in accordance with the teachings of this disclosure. As can be seen by comparingFIGS.7A through7C, the generated image708is of higher quality than the generated image704. Among other things, the toy train in the generated image708is somewhat clearer, and a smaller or no artifact is created at the back portion of the toy train where the clip is located. The tiles behind the toy train in the generated image708also include fewer artifacts and appear more square or rectangular than in the generated image704.

AlthoughFIGS.7A through7Cillustrate one example of results obtained using motion-aware NR-NeRF network training, various changes may be made toFIGS.7A through7C. For example, the contents of the images that are processed and generated can vary widely depending on the circumstances, such as the scene being imaged and the device or devices used to capture the images. Also, the results obtained using a standard NR-NeRF network and a NR-NeRF network200trained in accordance with the teachings of this disclosure can vary widely depending on the circumstances, such as depending on how well the NR-NeRF networks are trained and the quality of their training data. The results shown inFIGS.7A through7Care merely meant to illustrate one example of the type of results that may be obtained using the motion-aware machine learning model training techniques described in this patent document.

FIG.8illustrates an example method800for motion-aware NR-NeRF network training according to this disclosure. For ease of explanation, the method800is described as being performed by the server106in the network configuration ofFIG.1. However, the method800may be performed using any other suitable device(s) (such as the electronic device101) and in any other suitable system(s).

As shown inFIG.8, multiple training image frames of a scene are obtained at step802. This may include, for example, the processor120of the server106obtaining training image frames304from an imaging sensor array302or other source(s) of image frames of the scene. The training image frames304can capture the scene at multiple viewpoints and multiple viewing angles relative to the scene, and at least some of the training image frames304can capture motion within the scene. Multiple initial motion maps are generated using the training image frames at step804. This may include, for example, the processor120of the server106performing the multi-frame processing function502or other function to generate initial motion maps504. In some cases, an initial motion map504may be generated at least for each non-reference image frame among the training image frames304, such as when each initial motion map504identifies motion in a non-reference image frame relative to a reference image frame. 3D feature points associated with the scene are identified using the training image frames at step806. This may include, for example, the processor120of the server106performing the structure from motion determination function306to generate a point cloud308′ identifying (among other things) stationary points within the scene.

Initial motion masks are generated using the initial motion maps at step808. This may include, for example, the processor120of the server106performing the thresholding function506in order to apply at least one threshold to the initial motion maps504and generate initial motion masks602. Each of the initial motion masks is segmented to generate one or more sectionalized masks at step810. This may include, for example, the processor120of the server106performing the segmentation function604to generate sectionalized masks606, each of which may include a single continuous segment of the pixels identifying motion in the associated initial motion map504.

The 3D feature points are projected onto the sectionalized masks to produce projection motion masks at step812. This may include, for example, the processor120of the server106performing the projection function508in order to project the stationary points in the point cloud308′ onto the sectionalized masks606and generate projection motion masks510. Certain projection motion masks are selected and combined to generate tuned motion masks at step814. This may include, for example, the processor120of the server106performing the selection and combination function512to select the projection motion masks510having the smallest amount(s) of overlap between identified motion and the projected stationary points in order to generate final or tuned motion masks312. A machine learning model is trained using the training image frames and the tuned motion masks at step816. This may include, for example, the processor120of the server106performing the motion-aware NR-NeRF training function314to train the NR-NeRF network200. The machine learning model can be trained to generate 3D information about the scene (such as color data232and density data234), even from viewpoints and viewing angles not captured in the training image frames304. Moreover, the motion masks312can be used to guide the training of the machine learning model by differentiating between motion areas and non-motion areas within the training image frames304, which can (among other things) affect the amount of freedom given to the ray deformation network202when bending rays.

The trained machine learning model may be used in any suitable manner. For example, the trained machine learning model may be stored, output, or used at step818. This may include, for example, the processor120of the server106storing the trained NR-NeRF network200and using the trained NR-NeRF network200during inferencing. This may also or alternatively include the server106deploying the trained NR-NeRF network200to one or more other devices (such as the electronic device101) for inferencing. Among other things, the trained NR-NeRF network200can be used to generate 3D information for use by the rendering function236when generating images238.

AlthoughFIG.8illustrates one example of a method800for motion-aware NR-NeRF network training, various changes may be made toFIG.8. For example, while shown as a series of steps, various steps inFIG.8may overlap, occur in parallel, occur in a different order, or occur any number of times.

FIG.9illustrates an example method900for using a trained NR-NeRF network according to this disclosure. For ease of explanation, the method900is described as being performed by the electronic device101in the network configuration ofFIG.1. However, the method900may be performed using any other suitable device(s) (such as the server106) and in any other suitable system(s).

As shown inFIG.9, a specified viewpoint and a specified viewing angle associated with a scene are identified at step902. This may include, for example, the processor120of the electronic device101identifying the specified viewpoint and the specified viewing angle based on user input, based on the location of the electronic device101, or in any other suitable manner. The specified viewpoint and the specified viewing angle are provided to a machine learning model at step904. This may include, for example, the processor120of the electronic device101providing the specified viewpoint and the specified viewing angle to a trained NR-NeRF network200, where the NR-NeRF network200is trained using the motion-aware training techniques described above. As a result, the machine learning model can be trained to compensate for motion within the scene that is captured in training image frames304used to train the machine learning model.

3D information about the scene is generated using the machine learning model at step906. This may include, for example, the processor120of the electronic device101generating color data232and density data234using the trained NR-NeRF network200. An image of the scene is rendered from the specified viewpoint and at the specified viewing angle using the 3D information about the scene at step908. This may include, for example, the processor120of the electronic device101performing the rendering function236in order to generate an image238of the scene. The specified viewpoint and the specified viewing angle can be used to define an image plane on which pixels of the image238are rendered. The image may be used in any suitable manner. For example, the image may be stored, output, or used at step910. This may include, for example, the processor120of the electronic device101presenting the image238on the display160of the electronic device101, saving the image238to a camera roll stored in a memory130of the electronic device101, or attaching the image238to a text message, email, or other communication to be transmitted from the electronic device101. Note, however, that the image238could be used in any other or additional manner.

AlthoughFIG.9illustrates one example of a method900for using a trained NR-NeRF network, various changes may be made toFIG.9. For example, while shown as a series of steps, various steps inFIG.9may overlap, occur in parallel, occur in a different order, or occur any number of times.