Peking University

Yiting Shao

Ge Li

Qi Zhang

Wen Gao

Jing Wang

Chuang Ma

Jiamin Jin

The present invention provides a point cloud attribute entropy encoding method and device, and a point cloud attribute entropy decoding method and device. The encoding method comprises: if an overall encoding processing condition of point cloud attribute residual coefficients is satisfied, performing overall encoding processing on the point cloud attribute residual coefficients, and then ending the encoding; and if not, performing local one-by-one encoding processing on the point cloud attribute residual coefficients until the encoding is ended. The entropy decoding method comprises: if an overall decoding processing condition of the point cloud attribute residual coefficients is satisfied, performing overall decoding processing on the point cloud attribute residual coefficients, and then ending the decoding; and if not, performing local one-by-one decoding processing on the point cloud attribute residual coefficients until the decoding is ended. The present invention better utilizes redundant information in a point cloud and improves compression performance.

Point cloud attribute entropy encoding method and device, and point cloud attribute entropy decoding method and device

The present invention provides a point cloud intra prediction method and device based on weights optimization of neighbors. The invention relates to intra prediction for point cloud attribute compression, by optimizing the weights of the neighboring points on the basis of the density of the point cloud in three directions, i.e. x, y and z directions, and specifically, calculating the optimized weight of each neighboring point by optimizing corresponding coefficients of three coordinate components, i.e. x, y and z coordinate components of distances. The invention can improve the accuracy of intra prediction by means of enhancing the utilization of the overall geometric information of the point cloud, and then transformation, quantification and entropy are carried out on the prediction residuals, such that a better point cloud attribute compression performance is achieved.

Point cloud intra prediction method and device based on weights optimization of neighbors

An attribute-based point cloud strip division method. The method comprises: first, performing spatial division of a certain depth on a point cloud to obtain a plurality of local point clouds; and then, sorting the attribute values in the local point clouds, and on the basis of such, further performing point cloud strip division so as to obtain point cloud strips that have low geometric overhead and a uniform number of points. By means of comprehensively using the spatial position and attribute information of the point clouds, the points having similar attributes and related spatial positions are gathered as much as possible in one strip during strip division, which is convenient for making full use of the redundancy of the attribute information between adjacent points, and improving the performance of point cloud attribute compression. At the same time, independent coding between strips supports random access, improves coding efficiency, and prevents the accumulation and spread of coding errors, thus enhancing the fault tolerance of the system.

Attribute-based point cloud strip division method

A self-adaptive point cloud stripe division method. The method comprises: firstly, carrying out space division with a certain depth on a point cloud to obtain a plurality of local point clouds; then, counting the number of points in each of the local point clouds, comparing same with an upper and lower limit for the number of stripe points, and determining whether the number of points satisfies a requirement; and after a series of re-segmentation or re-fusion operations on the local point clouds, adjusting the number of points in each of the local point clouds until the number of points satisfies a range, thereby obtaining a final point cloud stripe. A plurality of local structures capable of being independently coded and decoded are obtained by means of division of a point cloud stripe, and this supports parallel processing, enhances system fault tolerance, and improves coding efficiency. Moreover, in consideration of the fact that the size of a point cloud stripe often has a limited range due to realistic factors of a large-scale point cloud, such as a limited transmission bandwidth, the method can satisfy such a requirement and make it possible to generate a plurality of point cloud stripes with a rational number of points while supporting parallel processing.

Self-adaptive point cloud stripe division method

Disclosed in the present invention is a point cloud attribution compression method based on deleting 0 elements in a quantisation matrix, including optimizing a traversal sequence for a quantisation matrix and deleting the 0 elements at the end of the data stream. The present invention may use seven types of traversal sequences at the encoding end of the point cloud attribute compression, such that the distribution of the 0 elements in the data stream may be more concentrated at the end thereof. The 0 elements at the end of the data stream may be deleted, removing redundant information and reducing the quantity of data to be entropy encoded. At the decoding end, the point cloud geometric information may be incorporated to supplement the deleted 0 elements and the quantisation matrix may be restored according to the traversal sequence, thereby improving compression performance without introducing new errors.

Point cloud attribute compression method based on deleting 0 elements in quantisation matrix

An enhanced graph transformation-based point cloud attribute compression method. For point cloud attribute information, a point cloud is first subjected to airspace division by using a K-dimension (KD) tree; a new graph transformation processing method in combination with spectral analysis is provided; the point cloud is then subjected to spectral clustering on graphs in coded blocks of the point cloud; expansion is performed on the basis of existing graph transformation to implement a local graph transformation scheme; enhanced graph transformation with two transformation modes is formed; the compression performance of graph transformation is improved. The method comprises: performing color space transformation of point cloud attributes; dividing the point cloud by using the KD tree to obtain the coded blocks; performing spectral clustering-based enhanced graph transformation; performing transformation mode decision; and performing uniform quantization and entropy coding. Provided is a new spectral analysis-based enhanced graph transformation scheme, wherein two transformation modes are comprised, and the optimal mode is selected by the mode decision; after the point cloud is divided with the tree, a graph is created in each coded block and the graph transformation is used as transformation mode I; on this basis, graph spectral clustering is implemented; the graph is divided into two local graphs and then local graph transformation is performed to serve as transformation mode II; in the enhanced graph transformation scheme supporting the two transformation modes, the optimal mode is selected by the mode decision to achieve the optimal performance of point cloud attribute compression.

Enhanced graph transformation-based point cloud attribute compression method

An intra-frame prediction-based point cloud attribute compression method. A new block structure-based intra-frame prediction scheme is provided for point cloud attribute information, where four prediction modes are provided to reduce information redundancy among different coding blocks as much as possible and improve point cloud attribute compression performance. The method comprises: performing point cloud attribute color space conversion; dividing a point cloud by using a K-dimensional (KD) tree to obtain coding blocks; performing block structure-based intra-frame prediction; performing intra-frame prediction mode division; performing conversion, uniform quantization, and entropy coding. The method relates to performing coding block division on a point cloud by using a KD tree, numbering coding blocks in a breadth-first traversal order, processing the coding blocks one by one in the numbering order by using a point cloud intra-frame prediction scheme having four prediction modes, selecting an optical prediction mode by means of mode decision, and performing conversion, quantization, and entropy coding on a predicted residual, so as to achieve optimal performance of point cloud attribute compression.

Intra-frame prediction-based point cloud attribute compression method

Provided is a point cloud attribute compression method based on a KD tree and optimized graph transformation, wherein same, with regard to point cloud data, reduces the influence of a sub-graph issue on the graph transformation efficiency by means of a new transformation block division method, optimizes a graph transformation kernel parameter, and improves the compression performance of the graph transformation, and comprises: point cloud pre-processing, point cloud KD tree division, graph construction in the transformation block, graph transformation kernel parameter training, and a point cloud attribute compression process. The present invention optimizes the division method for a point cloud transformation block, and makes the number of points in the transformation block the same, and also realizes that the dimensionality of a transformation matrix is basically the same, so as to facilitate parallel processing of subsequent graph transformations; also optimizes the graph establishment in the transformation block, and avoids the sub-graph issue caused by the existing method; and at the same time optimizes, by training the kernel parameter of the graph transformation, the sparsity of a graph transformation Laplacian matrix, so as to achieve a better point cloud attribute compression performance.

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Guangdong, China

Yiting Shao