Abstract: In various examples, images (e.g., novel views) of an object may be rendered using an optimized number of samples of a 3D representation of the object. The optimized number of the samples may be determined based at least on casting rays into a scene that includes the 3D representation of the object and/or an acceleration data structure corresponding to the object. The acceleration data structure may include features corresponding to characteristics of the object, and the features may be indicative of the number of samples to be obtained from various portions of the 3D representation of the object to render the images. In some examples, the 3D representation may be a neural radiance field that includes, as a neural output, a spatially varying kernel size predicting the characteristics of the object, and the features of the acceleration data structure may be related to the spatially varying kernel size.

Abstract: In various examples, a system can include one or more processors to segment, using at least one machine learning model, a first representation of an object into a plurality of parts according to a hierarchy, where the hierarchy indicates groups of datapoints for at least one part of the plurality of parts based on a value. The one or more processors generate at least one second representation of the object based on the at least one of the part of the plurality of parts.

Abstract: Apparatuses, systems, and techniques to use neural networks to predict vertices to add to a surface mesh of a three-dimensional (3D) object. In at least one embodiment, one or more neural networks predict one or more verticies to add to a 3D object based, at least in part, on different vertices removed from surface meshes of different 3D objects.

Abstract: In various examples, images (e.g., novel views) of an object may be rendered using an optimized number of samples of a 3D representation of the object. The optimized number of the samples may be determined based at least on casting rays into a scene that includes the 3D representation of the object and/or an acceleration data structure corresponding to the object. The acceleration data structure may include features corresponding to characteristics of the object, and the features may be indicative of the number of samples to be obtained from various portions of the 3D representation of the object to render the images. In some examples, the 3D representation may be a neural radiance field that includes, as a neural output, a spatially varying kernel size predicting the characteristics of the object, and the features of the acceleration data structure may be related to the spatially varying kernel size.

Abstract: Various examples, systems, and methods are disclosed relating to reconstructing, segmenting, and/or simulating pipeline. A first computing system can obtain video data including a depth map of a scene. The first computing system can reconstruct a three-dimensional (3D) representation of the scene using at least one or more Gaussian splat representations of one or more objects in the scene and the depth map. The first computing system can segment at least one object in the 3D representation. The first computing system can update at least one of the plurality of regions of the 3D representation within a threshold distance of the at least one object in the scene. The first computing system can generate at least one image of the 3D representation that depicts at least a portion of the at least one object for display.

Abstract: Various examples, systems, and methods are disclosed relating to reconstructing, segmenting, and/or simulating pipeline. A first computing system can obtain at least one object segmented from video data. The first computing system can densify the at least one object. The first computing system can simulate one or more interactions of the voxelized volume of the at least one densified object to update at least one physical attribute of the at least one object. The first computing system can generate at least one image depicting at least a portion of the at least one object with the at least one updated physical attribute.

Abstract: Various examples, systems, and methods are disclosed relating to texture synthesis. A first computing system determine, using a denoiser and based at least on an input indicating one or more characteristics of a scene, a plurality of estimated views of the scene corresponding to a texture. The first computing system can render, from a model of the texture, a plurality of renders of the texture, at least one render of the plurality of renders being associated with a corresponding estimated view of the plurality of estimated views. The first computing system can update the model of the texture based at least on the plurality of renders and the plurality of estimated views. The first computing system can update the plurality of estimated views based at least on the plurality of renders.

Abstract: Aspects of this technical solution can allocate one or more portions of a mesh in a three-dimensional (3D) space to one or more processing units associated with the one or more circuits, the mesh associated with a surface of an object in the 3D space, transform, by the one or more of the processing units, one or more of the portions of the mesh from the 3D space into corresponding second meshes in a two-dimensional (2D) space, segment, by the one or more of the processing units, according to a determination that a distortion of a portion of the mesh among the one or more of the portions of the mesh is below a threshold of parameterization, the portion of the mesh into two further portions of the mesh, where the further portions are among the one or more portions of the mesh, and generate, according to a determination that the distortion of the portion of the mesh among the one or more of the portions of the mesh is at or above the threshold of parameterization, an output mesh including the one or more portion

Abstract: In various examples, images (e.g., novel views) of an object may be rendered using an optimized number of samples of a 3D representation of the object. The optimized number of the samples may be determined based at least on casting rays into a scene that includes the 3D representation of the object and/or an acceleration data structure corresponding to the object. The acceleration data structure may include features corresponding to characteristics of the object, and the features may be indicative of the number of samples to be obtained from various portions of the 3D representation of the object to render the images. In some examples, the 3D representation may be a neural radiance field that includes, as a neural output, a spatially varying kernel size predicting the characteristics of the object, and the features of the acceleration data structure may be related to the spatially varying kernel size.

Abstract: Aspects of this technical solution can obtain, according to a plurality of cameras oriented toward the surface of a three-dimensional (3D) model having a surface including a two-dimensional (2D) texture model, input according to corresponding views from the plurality of cameras of the 2D texture model on the surface of the 3D model, and generate, according to the input and according to a model configured to generate a two-dimensional (2D) image, an output including a 2D texture for the 3D model, the output responsive to receiving an indication of the 3D model and the 2D texture.