Abstract: A method for rate distortion optimization for adaptive sub-band coding of Region-Adaptive Hierarchical Transform (RAHT) coefficients, which is a point cloud color compression method adopted in PCC test model TMC13 for compression of CAT1 sequences is described herein. Based on Lagrangian optimization, the method estimates the coefficient distortion by using the compressed mesh geometry as reference. The Lagrange factor is calculated based on the quantization parameters, and the results show that the parameters obtained automatically have similar or better performance than parameters chosen after an extensive search.

Abstract: Aspects of the disclosure provide methods and apparatuses for point cloud compression and decompression. In some examples, an apparatus for point cloud compression/decompression includes processing circuitry. For example, the processing circuitry determines, from a point cloud, at least a first set of candidate source points that are associated with a target point in a geometry image reconstructed from a compressed geometry image for the point cloud. Then, the processing circuitry weights a color of each candidate source point in the first set based on a combination of a location difference between the candidate source point and the target point, and a color difference between the color of the candidate source point and an aggregate color of the first set of candidate source points. Further, the processing circuitry determines a color of the target point based on a first weighted color average of the first set of candidate source points.

Abstract: According to some aspects of the disclosure, processing circuitry for point cloud decompression can decode prediction information of an image from a coded bitstream corresponding to a point cloud. The prediction information indicates that the image includes a first portion corresponding to patches in the point cloud, and a second portion corresponding to missed points by the patches from the point cloud. The processing circuitry can scan the second portion of the image according to a non-jumpy scan to obtain the missed points in a sequence, and reconstruct the point cloud based on reconstructed patches corresponding to the patches and reconstructed missed points based on the sequence of the missed points.

Abstract: An apparatus receives a video which includes at least one three-dimensional (3D) object in a 3D physical space. A 3D geometrical representation of a point cloud is generated based on the video. The 3D geometrical representation of the point cloud includes a first set of points associated with geometrical information and texture information corresponding to the at least one 3D object. A plurality of two-dimensional (2D) projections are generated from the 3D geometrical representation of the point cloud. A second set of points that are occluded in the first set of points is detected, corresponding to the plurality of 2D projections. The plurality of 2D projections and the second set of points are distinctly encoded, and the remaining points, other than the detected second set of points, in the first set of points are discarded for efficient compression of the 3D geometrical representation of the point cloud.

Abstract: A method includes receiving a data cloud including a plurality of data points. The method further includes identifying each data point including a region-of-interest (ROI) and dividing the data cloud into a ROI cloud and one or more non-ROI clouds. The method includes performing a patch generation process on the ROI cloud, the patch generation process including generating a ROI patch from each data point including the ROI. The method includes performing a patch packing process on the ROI cloud, the patch packing process including: (i) mapping each ROI patch to a two dimensional (2D) map, (ii) determining whether at least two ROI patches from the plurality of ROI patches are located in more than one tile of the map, and (iii) in response to the determination that at least two ROI patches are located in more than one tile, moving each of the ROI patches to a tile.

Abstract: A method of adaptive point cloud attribute coding includes obtaining an attribute of a current point included in point cloud data, and obtaining candidate predicted values of the obtained attribute, the candidate predicted values including any one or any combination of a weighted average value of a plurality of distances from the current point respectively to other points included in the point cloud data, a first predicted value of a first distance from the current point to a first nearest point among the other points and a second predicted value of a second distance from the current point to a second nearest point after the first nearest point among the other points. The method further includes selecting, for the obtained attribute, one among the obtained candidate predicted values, using rate-distortion optimization, and setting, for a decoder, a flag indicating whether the obtained candidate predicted values includes the weighted average value.

Abstract: A method and apparatus for referencing a parameter set to permit decoding of a coded point cloud sequence includes parsing a parameter set associated with the coded point cloud sequence. A dependent parameter set is identified based on parsing the parameter set. It is identified whether the dependent parameter set is a patch-level parameter set. The patch-level parameter set is referenced based on identifying that the dependent parameter set is the patch-level parameter set to permit the decoding of the coded point cloud sequence. A frame-level parameter set is referenced based on identifying that the dependent parameter set is not the patch-level parameter set to permit the decoding of the coded point cloud sequence.

Abstract: A method of predicting point cloud attribute coding includes receiving point cloud data comprising one or more candidate points and extracting one or more candidate attribute values for the one or more candidate points from the received point cloud data. The method further includes calculating a weighted median from the one or more extracted candidate attribute values and a weight factor, whereby the weight factor corresponds to a relative importance factor of a candidate vector associated with the extracted candidate attribute values. The method further includes assign one or more prediction indices corresponding to attribute data for video encoding to the candidate points in response to the calculation of the weighted median, wherein the prediction indices and processing the media content to encode the attribute data associated with the candidate points having the lowest prediction indices.

Abstract: A method of obtaining a tree-based point cloud compression (PCC) media stream using moving picture experts group (MPEG)-dynamic adaptive streaming over http (DASH), is performed by at least one processor and includes obtaining, from a server, a media representation description (MPD) comprising a PCC uniform resource indicator (URI) and a PCC three-dimensional (3D) descriptor, and determining whether the PCC URI comprised in the obtained MPD indicates that the tree-based PCC media stream is to be accessed. The method further includes, based on the PCC URI being determined to indicate that the tree-based PCC media stream is to be accessed, obtaining, from the server, the tree-based PCC media stream, using the PCC 3D descriptor comprised in the obtained MPD.

Abstract: A method of interframe point cloud attribute coding is performed by at least one processor and includes obtaining, as a motion estimation unreliability measure of motion estimation of a target frame, a value inversely proportional to a ratio of a number of first point cloud samples of the target frame respectively with second point cloud samples of an interframe reference frame, to a number of point cloud samples in the target frame. The method further includes identifying whether the obtained motion estimation unreliability measure is greater than a predetermined threshold, based on the obtained motion estimation unreliability measure being identified to be greater than the predetermined threshold, skipping motion compensation of the target frame, and based on the obtained motion estimation unreliability measure being identified to be less than or equal to the predetermined threshold, performing the motion compensation of the target frame.

Abstract: A method of point cloud attribute coding includes calculating a motion-estimating uncertainty value based on geometry data associated with a point cloud; identifying one or more inter-frame nearest neighbor point cloud samples corresponding to the point cloud in response to a determination that the motion-estimating uncertainty value is less than a threshold value; ranking one or more temporal candidate points associated with the one or more identified inter-frame nearest neighbor point cloud samples based on the motion-estimation uncertainty value; and extracting one or more sample attribute values from the one or more temporal candidate points, whereby the one or more sample attribute values correspond to the geometry data.

Abstract: Systems and methods for decoding a video stream, a method includes receiving video stream, the video stream including: a frame of a plurality of two-dimensional (2D) pictures that are layers of the frame, each of the plurality of 2D pictures having a respective attribute of a same three-dimensional (3D) representation, frame partition information that indicates the frame is partitioned into a plurality of sub-frames, each of the plurality of sub-frames being a respective combination of a sub-region of each picture of the plurality of 2D pictures, and 3D bounding box information that specifies a 3D position corresponding to a sub-frame of the plurality of sub-frames; the method further includes identifying the sub-frame using the frame partition information; and decoding the sub-frame identified.

Abstract: Aspects of the disclosure provide methods and apparatuses for point cloud compression and decompression. In some examples, an apparatus for point cloud compression/decompression includes processing circuitry. For example, the apparatus is for point cloud decompression. The processing circuitry decodes prediction information of an image from a coded bitstream corresponding to a point cloud. The prediction information indicates that the image includes a plurality of missed points from at least a patch for the point cloud, and the plurality of missed points are arranged in the image according to a non-jumpy scan. Then, the processing circuitry reconstructs the plurality of missed points from the image according to the non-jumpy scan.

Abstract: Aspects of the disclosure provide methods and apparatuses for point cloud compression and decompression. In some examples, an apparatus for point cloud compression/decompression includes processing circuitry. In some embodiments, the processing circuitry decodes prediction information of a point cloud from a coded bitstream and reconstructs a geometry reconstructed cloud according to a geometry image of the point cloud that is decoded from the coded bitstream. Further, the processing circuitry applies a filter to at least a geometry sample inside a patch of the geometry reconstructed cloud in addition to boundary samples of the patch to generate a smoothed geometry reconstructed cloud, and reconstructs points of the point cloud based on the smoothed geometry reconstructed cloud.

Abstract: A method and apparatus for decoding a video stream encoded using video point cloud coding, the decoding including obtaining a geometry-reconstructed point cloud based on one or more patches; identifying a first boundary of a patch including a plurality of first boundary points; identifying a second boundary including a plurality of second boundary points inside the first boundary; performing smoothing on the first boundary points and the second boundary points; obtaining a smoothed geometry-reconstructed point cloud based on the smoothed first boundary points and the smoothed second boundary points; and reconstructing a dynamic point cloud using the smoothed geometry-reconstructed point cloud.

Abstract: Aspects of the disclosure provide methods and apparatuses for point cloud compression and decompression. In some examples, an apparatus for point cloud compression/decompression includes processing circuitry. For example, the processing circuitry of the apparatus for point cloud encoding determines, from a point cloud, more than two candidate source points that are associated with a target point in a reconstructed geometry reconstructed from a compressed geometry image for the point cloud. Then the processing circuitry determines a color for the target point based on colors of the more than two candidate source points, and encodes texture of the point cloud with the target point having the determined color.

Abstract: Aspects of the disclosure provide methods and apparatuses for point cloud compression and decompression. In some examples, an apparatus for point cloud compression/decompression includes processing circuitry. For example, the processing circuitry in the apparatus for point cloud encoding receives an occupancy map for a point cloud. The occupancy map is indicative of a background portion and a foreground portion for a coding block in an image that is generated based on the point cloud. Then, the processing circuitry devaluates distortions in the background portion of the coding block during an optimization process that results a coding option for the coding block, and encodes the coding block according to the coding option.

Abstract: The present invention provides a method for performing transformation using a layered Givens transform (LGT), comprising the steps of: deriving at least one rotation layer and at least one permutation layer on the basis of a given transform matrix (H) and a given error parameter; acquiring a LGT coefficient on the basis of the rotation layer and the permutation layer; and quantizing and entropy-encoding the LGT coefficient, wherein the permutation layer comprises a permutation matrix obtained by permuting a row of an identity matrix.

Abstract: A method for reducing color leaking artefacts in an image formed by projection processing from a 3D point cloud comprises: receiving an input image comprising the 3D point cloud; classifying the cloud into a plurality of surface patches; projecting the patches onto a plane to form a first 2D image; processing the first 2D image, by coding, transmitting and decoding, to form a final 2D image; and providing the final 2D image as an output. Processing includes independent patch processing to reduce inter-patch color leakage in the final 2D image, the independent patch processing including chroma sub-sampling pixels within each of the projected patches in the first 2D image separately; recombining the chroma sub-sampled patches to form a second 2D image; and compressing the second 2D image.

Abstract: A point cloud compression approach that exploits HEVC for compression of both the geometry and color data is described herein. Although both the geometry and color could be compressed in a lossy fashion, the point cloud compression approach is suggested for the scenario of lossless geometry and lossy color coding. Both geometry and color data are first mapped into 2D images and then compressed by HEVC. A sorting technique is used to sort the geometry data to make it as correlated as possible when it is mapped to a 2D image. For color coding, clustering is used to put similar colors in a point cloud into spatial neighbors in the mapped 2D image. This significantly avoids color leaking due to quantization errors to neighbor points in 3D. The results show that much better compression is achieved compared to the Anchor when it is configured for lossless geometry coding.