Abstract: A system comprises an encoder configured to compress attribute information and/or spatial for a point cloud and/or a decoder configured to decompress compressed attribute and/or spatial information for the point cloud. To compress the attribute and/or spatial information, the encoder is configured to convert a point cloud into an image based representation. Also, the decoder is configured to generate a decompressed point cloud based on an image based representation of a point cloud.

Abstract: A system comprises an encoder configured to compress attribute and/or spatial information for a point cloud and/or a decoder configured to decompress compressed attribute and/or spatial information for the point cloud. To compress the attribute and/or spatial information, the encoder is configured to convert a point cloud into an image based representation. Also, the decoder is configured to generate a decompressed point cloud based on an image based representation of a point cloud. In some embodiments, an encoder applies an attribute transfer function to attributes of an image based representation with out-of-loop smoothing such that a decoder may apply any smoothing filter supported by the decoder without being restricted to using a similar smoothing filter as the encoder.

Abstract: A system comprises an encoder configured to compress attribute information for a point cloud and/or a decoder configured to decompress compressed attribute information for the point cloud. Attribute values for at least one starting point are included in a compressed attribute information file and attribute correction values used to correct predicted attribute values are included in the compressed attribute information file. Attribute values are predicted based, at least in part, on attribute values of neighboring points and distances between a particular point for whom an attribute value is being predicted and the neighboring points. The predicted attribute values are compared to attribute values of a point cloud prior to compression to determine attribute correction values. A decoder follows a similar prediction process as an encoder and corrects predicted values using attribute correction values included in a compressed attribute information file.

Abstract: A system comprises an encoder configured to compress attribute information and/or spatial information for volumetric visual content and/or a decoder configured to decompress compressed attribute and/or spatial information for the volumetric visual content. The encoder is configured to convert a 3D representation of the visual volumetric content into a 2D image based representation. The encoder is further configured to scale the patch in 2D space independent of any scaling in 3D space. Auxiliary information is signaled for use in identifying 2D scaled or unscaled patches in an image frame, mapping the patches into 3D space, and adjusting for any scaling factors applied at the encoder.

Abstract: A system comprises an encoder configured to compress attribute information and/or spatial for a point cloud and/or a decoder configured to decompress compressed attribute and/or spatial information for the point cloud. A point cloud attribute transfer algorithm may be used to determine distortion between an original point cloud and a reconstructed point cloud. Additionally, the point cloud attribute transfer algorithm may be used to select attribute values for a reconstructed point cloud such that distortion between an original point cloud and a reconstructed version of the original point cloud is minimized.

Abstract: Support for additional components may be specified in a coding scheme for image data. A layer of a coding scheme that specifies color components may also specify additional components. Characteristics of the components may be specified in the same layer or a different layer of the coding scheme. An encoder or decoder may identify the specified components and determine the respective characteristics to perform encoding and decoding of image data.

Abstract: In an example method, a system obtains first data representing a plurality of polygons of a polygon mesh, and performs several operations for each of the polygons, including (i) determining a number of sample points for that polygon, where the number of sample points is determined based on at least one of an area of that polygon or an area of the polygon mesh, (ii) determining a distribution of the sample points for that polygon, and (iii) sampling the polygon mesh in accordance with the determined number of sample points and the determined distribution of sample points, where sampling the polygon mesh includes determining one or more characteristics of the polygon mesh at each of the sample points. The system also outputs second data representing the one or more characteristics of the polygon mesh at one or more of the sample points.

Abstract: A system comprises an encoder configured to compress attribute information for a point cloud and/or a decoder configured to decompress compressed attribute information. Attribute values for at least one starting point are included in a compressed attribute information file and attribute correction values are included in the compressed attribute information file. An order for the points is determined based on a space filling curve, wherein an encoder and a decoder determine a same order for the points based on the space filling curve. Levels of detail are determined by sampling the ordered points according to different sampling parameters, and attribute values are predicted for the points in the levels of detail using the determined order. The encoder determines attribute correction values based on a comparison of the predicted values to an original value prior to compression. The decoder corrects the predicted attribute values based on received attribute correction values.

Abstract: An example method includes receiving (502) a plurality of points that represent a point cloud; representing a position of the point in each dimension of a three-dimensional space as a sequence of bits (504), where the position of the point is encoded according to a tree data structure; partitioning (506) at least one of the sequences of bits into a first portion of bits and a second portion of bits; quantizing (508) each of the second portions of bits according to a quantization step size, where the quantization step size is determined according to an exponential function having a quantization parameter value as an input and the quantization step size as an output; and generating (510) a data structure representing the point cloud and including the quantized second portions of bits.

Abstract: An example device includes one or more processors, and memory storing instructions that when executed by the processors, cause the processors to receive points that represent a point cloud in three-dimensional space, and generate a data structure representing the point cloud. Generating the data structure includes encoding a position of each point in each dimension as a sequence of bits according to a tree data structure; partitioning each of the sequences into two or more portions according to a scaling depth; determining that a subset of the points is spatially isolated from a remainder of the points; quantizing each of the portions associated with the subset of the points according to a first quantization step size; quantizing each of the portions associated with the remainder of the points according to a second quantization step size; and including the quantized portions in the data structure.

Abstract: A system comprises an encoder configured to compress and encode data for a three-dimensional mesh using a video encoding technique. To compress the three-dimensional mesh, the encoder determines sub-meshes and for each sub-mesh: texture patches and geometry patches. Also the encoder determines patch connectivity information and patch texture coordinates for the texture patches and geometry patches. The texture patches and geometry patches are packed into video image frames and encoded using a video codec. Additionally, the encoder determines boundary stitching information for the sub-meshes. A decoder receives a bit stream as generated by the encoder and reconstructs the three-dimensional mesh.

Abstract: A system comprises an encoder configured to compress attribute and/or spatial information for a point cloud and/or a decoder configured to decompress compressed attribute and/or spatial information for the point cloud. To compress the attribute and/or spatial information, the encoder is configured to convert a point cloud into an image based representation. Also, the decoder is configured to generate a decompressed point cloud based on an image based representation of a point cloud. In some embodiments, an encoder generates time-consistent patches for multiple version of the point cloud at multiple moments in time and uses the time-consistent patches to generate image based representations of the point cloud at the multiple moments in time.

Abstract: Virtual Reality (VR) processing devices and methods are provided for transmitting user feedback information comprising at least one of user position information and user orientation information, receiving encoded audio-video (A/V) data, which is generated based on the transmitted user feedback information, separating the A/V data into video data and audio data corresponding to a portion of a next frame of a sequence of frames of the video data to be displayed, decoding the portion of a next frame of the video data and the corresponding audio data, providing the audio data for aural presentation and controlling the portion of the next frame of the video data to be displayed in synchronization with the corresponding audio data.

Abstract: A system comprises an encoder configured to compress attribute information for a point cloud and/or a decoder configured to decompress compressed attribute information for the point cloud. Attribute values for at least one starting point are included in a compressed attribute information file and attribute correction values used to correct predicted attribute values are included in the compressed attribute information file. Attribute values are predicted based, at least in part, on attribute values of neighboring points and distances between a particular point for whom an attribute value is being predicted and the neighboring points. The predicted attribute values are compared to attribute values of a point cloud prior to compression to determine attribute correction values. A decoder follows a similar prediction process as an encoder and corrects predicted values using attribute correction values included in a compressed attribute information file.

Abstract: A system comprises an encoder configured to compress attribute information and/or spatial information for a point cloud and/or a decoder configured to decompress compressed attribute and/or spatial information for the point cloud. The encoder is configured to convert a point cloud into an image based representation. The encoder packs patch images into an image frame and fills empty spaces in the image frame with a padding. The encoder is also configured to determine quantized minimum depths and/or maximum depths patch images in the image frames, wherein depth information is signaled relative to the quantized minimum depth.

Abstract: An encoder is configured to compress point cloud geometry information using a prediction tree. Ancestor nodes of a node added to the prediction tree may be used to determine the predicted value of the node to be added according to a prediction technique. The prediction tree may be encoded and may be provided for transmission to a decoder that can regenerate the point cloud.

Abstract: A search space for performing nearest neighbor searches for encoding point cloud data may be trimmed. Ranges of a space filling curve may be used to identify search space to exclude or reuse, instead of generating nearest neighbor search results for at least some of the points of a point cloud located within some of the ranges of the space filling curve. Additionally, neighboring voxels may be searched to identify any neighboring points missed during the trimmed search based on the ranges of the space filling curve.

Abstract: A system comprises an encoder configured to compress attribute information and/or spatial for a point cloud and/or a decoder configured to decompress compressed attribute and/or spatial information for the point cloud. To compress the attribute and/or spatial information, the encoder is configured to convert a point cloud into an image based representation. Also, the decoder is configured to generate a decompressed point cloud based on an image based representation of a point cloud. The encoder generates an occupancy map and may also encode the occupancy map as an image based representation. In some embodiments, a video encoder encodes image based representations of spatial information for the points of the point cloud, image based representations of attribute values for points of the point cloud, and an image based representation of an occupancy map for the spatial and attribute images.

Abstract: An exemplary system receives first data representing one or more buildings, and generates second data representing the one or more buildings. Generating the second data includes, for each of the one or more buildings: (i) determining, based on the first data, a plurality of first edges defining an exterior surface of at least a portion of the building, where the first edges interconnect at a plurality of first points, (ii) encoding, in the second data, information corresponding to the quantity of the first points, (iii) encoding, in the second data, an absolute position of one of the first points, and (iv) for each of the remaining first points, encoding, in the second data, a position of that first point relative to a position of at least another one of the first points. The system outputs the second data.

Abstract: A system comprises an encoder configured to compress attribute information and/or spatial for a point cloud and/or a decoder configured to decompress compressed attribute and/or spatial information for the point cloud. To compress the attribute and/or spatial information, the encoder is configured to convert a point cloud into an image based representation. Also, the decoder is configured to generate a decompressed point cloud based on an image based representation of a point cloud.