Abstract: An encoder is configured to compress point cloud geometry information using an octree geometric compression technique that utilizes a binary arithmetic encoder, a look-ahead table, a cache, and a context selection process, wherein encoding contexts are selected based, at least in part, on neighborhood configurations. In a similar manner, a decoder is configured to decode compressed point cloud geometry information utilizing a binary arithmetic encoder, a look-ahead table, a cache, and a context selection process.

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. Various compression strategies may be used to encode an occupancy map and related block-to-patch information indicating which portions of the image frame correspond to which packed patches. Packed image frames comprising patches and padding, along with an encoded occupancy map and related block-to-patch information are sent to a decoder. The decoder is configured to generate a decompressed point cloud based on the packed image frames, compressed occupancy map, and related block-to-patch information.

Abstract: A system comprises an encoder configured to compress attribute information for a point cloud and/or a decoder configured to decompress compressed attribute for the point cloud. To compress the attribute information, attribute values are predicted using one of a plurality of prediction strategies, wherein a selected prediction strategy is selected based at least in part on attribute variability of points in a neighborhood of points. A decoder follows a similar prediction process. Also, attribute correction values may be determined to correct predicted attribute values and may be used by a decoder to decompress a point cloud, wherein the decoder applies the same prediction strategy applied at the encoder.

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 performs downscaling of an image frame prior to video encoding and a decoder performs upscaling of an image frame subsequent to video decoding.

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. A processing/filtering element utilizes occupancy map information and/or auxiliary patch information to determine relationships between patches in image frames and adjusts encoding/decoding and/or filtering or pre/post-processing parameters based on the determined relationships.

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: 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: 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: A system comprises an encoder configured to compress attribute information for a point cloud and/or a decoder configured to decompress compressed attribute for the point cloud. To compress the attribute information, multiple levels of detail are generated based on spatial information. Also, attribute values are predicted based on the level of details. A decoder follows a similar prediction process based on level of details. Also, attribute correction values may be determined to correct predicted attribute values and may be used by a decoder to decompress a point cloud compressed using level of detail attribute compression. In some embodiments, an update operation is performed to smooth attribute correction values taking into account an influence factor of respective points in a given level of detail on attributes in other levels of detail.

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. In some embodiments, an encoder may be configured to further compress points omitted from the image based representation. Also, in some embodiments, a decoder may be configured to decode points compressed outside of an image based representation or in a separate image based representation.

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: 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. Various compression strategies may be used to encode an occupancy map and related block-to-patch information indicating which portions of the image frame correspond to which packed patches. Packed image frames comprising patches and padding, along with an encoded occupancy map and related block-to-patch information are sent to a decoder. The decoder is configured to generate a decompressed point cloud based on the packed image frames, compressed occupancy map, and related block-to-patch information.

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 an octree geometric compression technique that utilizes a binary arithmetic encoder, a look-ahead table, a cache, and a context selection process, wherein encoding contexts are selected based, at least in part, on neighborhood configurations. In a similar manner, a decoder is configured to decode compressed point cloud geometry information utilizing a binary arithmetic encoder, a look-ahead table, a cache, and a context selection process.

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. 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, wherein pixel values for the padding are determined based on neighboring pixels values such that the padding is smoothed in the image frame. 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 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. Various compression strategies may be used to encode an occupancy map and related block-to-patch information indicating which portions of the image frame correspond to which packed patches. Packed image frames comprising patches and padding, along with an encoded occupancy map and related block-to-patch information are sent to a decoder. The decoder is configured to generate a decompressed point cloud based on the packed image frames, compressed occupancy map, and related block-to-patch information.

Abstract: An encoder is configured to compress point cloud geometry information using an octree geometric compression technique that utilizes a binary arithmetic encoder, a look-ahead table, a cache, and a context selection process, wherein encoding contexts are selected based, at least in part, on neighborhood configurations. In a similar manner, a decoder is configured to decode compressed point cloud geometry information utilizing a binary arithmetic encoder, a look-ahead table, a cache, and a context selection process.

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. Attribute values are predicted based, at least in part, on attribute values of neighboring points. The predicted attribute values are compared to attribute values of a point cloud prior to compression to determine attribute correction values. In order to improve computing efficiency and/or repeatability, fixed-point number representations are used when determining predicted attribute values and 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 using fixed-point number representations.

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 performs downscaling of an image frame prior to video encoding and a decoder performs upscaling of an image frame subsequent to video decoding.

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. To compress the attribute information, multiple levels of detail are generated based on an ordering of the points according to a space filling curve and attribute values are predicted. The attribute values may be predicted simultaneously while points are being assigned to different levels of detail. A decoder follows a similar prediction process based on level of details. Also, attribute correction values may be determined to correct predicted attribute values and may be used by a decoder to decompress a point cloud compressed using level of detail attribute compression. In some embodiments, attribute correction values may take into account an influence factor of respective points in a given level of detail on attributes in other levels of detail.