Characterized by geometry and photometry attributes, point cloud has been widely applied in the immersive services of various 3Dobjects and scenes. The development of even more precise capture devices and the increasi...Characterized by geometry and photometry attributes, point cloud has been widely applied in the immersive services of various 3Dobjects and scenes. The development of even more precise capture devices and the increasing requirements for vivid rendering in-evitably induce huge point capacity, thus making the point cloud compression a demanding issue. In this paper, we introduce sev-eral well-known compression algorithms in the research area as well as the boosting industry standardization works. Specifically,based on various applications of this 3D data, we summarize the static and dynamic point cloud compression, both including irreg-ular geometry and photometry information that represent the spatial structure information and corresponding attributes, respective-ly. In the end, we conclude the point cloud compression as a promising topic and discuss trends for future works.展开更多
In this paper,a novel compression framework based on 3D point cloud data is proposed for telepresence,which consists of two parts.One is implemented to remove the spatial redundancy,i.e.,a robust Bayesian framework is...In this paper,a novel compression framework based on 3D point cloud data is proposed for telepresence,which consists of two parts.One is implemented to remove the spatial redundancy,i.e.,a robust Bayesian framework is designed to track the human motion and the 3D point cloud data of the human body is acquired by using the tracking 2D box.The other part is applied to remove the temporal redundancy of the 3D point cloud data.The temporal redundancy between point clouds is removed by using the motion vector,i.e.,the most similar cluster in the previous frame is found for the cluster in the current frame by comparing the cluster feature and the cluster in the current frame is replaced by the motion vector for compressing the current frame.The hrst,the B-SHOT(binary signatures of histograms orientation)descriptor is applied to represent the point feature for matching the corresponding point between two frames.The second,the K-mean algorithm is used to generate the cluster because there are a lot of unsuccessfully matched points in the current frame.The matching operation is exploited to find the corresponding clusters between the point cloud data of two frames.Finally,the cluster information in the current frame is replaced by the motion vector for compressing the current frame and the unsuccessfully matched clusters in the curren t and the motion vectors are transmit ted into the rem ote end.In order to reduce calculation time of the B-SHOT descriptor,we introduce an octree structure into the B-SHOT descriptor.In particular,in order to improve the robustness of the matching operation,we design the cluster feature to estimate the similarity bet ween two clusters.Experimen tai results have shown the bet ter performance of the proposed method due to the lower calculation time and the higher compression ratio.The proposed met hod achieves the compression ratio of 8.42 and the delay time of 1228 ms compared with the compression ratio of 5.99 and the delay time of 2163 ms in the octree-based compression method under conditions of similar distortion rate.展开更多
Recent years have witnessed that 3D point cloud compression(PCC)has become a research hotspot both in academia and industry.Especially in industry,the Moving Picture Expert Group(MPEG)has actively initiated the develo...Recent years have witnessed that 3D point cloud compression(PCC)has become a research hotspot both in academia and industry.Especially in industry,the Moving Picture Expert Group(MPEG)has actively initiated the development of PCC standards.One of the adopted frameworks called geometry-based PCC(G-PCC)follows the architecture of coding geometry first and then coding attributes,where the region adaptive hierarchical transform(RAHT)method is introduced for the lossy attribute compression.The upsampled transform domain prediction in RAHT does not sufficiently explore the attribute correlations between neighbor nodes and thus fails to further reduce the attribute redundancy between neighbor nodes.In this paper,we propose a subnode-based prediction method,where the spatial position relationship between neighbor nodes is fully considered and prediction precision is further promoted.We utilize some already-encoded neighbor nodes to facilitate the upsampled transform domain prediction in RAHT by means of a weighted average strategy.Experimental results have illustrated that our proposed attribute compression method shows better rate-distortion(R-D)performance than the latest MPEG G-PCC(both on reference software TMC13-v22.0 and GeS-TM-v2.0).展开更多
In Video-based Point Cloud Compression(V-PCC),2D videos to be encoded are generated by 3D point cloud projection,and compressed by High Efficiency Video Coding(HEVC).In the process of 2D video compression,the best mod...In Video-based Point Cloud Compression(V-PCC),2D videos to be encoded are generated by 3D point cloud projection,and compressed by High Efficiency Video Coding(HEVC).In the process of 2D video compression,the best mode of Coding Unit(CU)is searched by brute-force strategy,which greatly increases the complexity of the encoding process.To address this issue,we first propose a simple and effective Portable Perceptron Network(PPN)-based fast mode decision method for V-PCC under Random Access(RA)configuration.Second,we extract seven simple hand-extracted features for input into the PPN network.Third,we design an adaptive loss function,which can calculate the loss by allocating different weights according to different Rate-Distortion(RD)costs,to train our PPN network.Finally,experimental results show that the proposed method can save encoding complexity of 43.13%with almost no encoding efficiency loss under RA configuration,which is superior to the state-of-the-art methods.The source code is available at https://github.com/Mesks/PPNforV-PCC.展开更多
Point cloud compression is critical to deploy 3D representation of the physical world such as 3D immersive telepresence,autonomous driving,and cultural heritage preservation.However,point cloud data are distributed ir...Point cloud compression is critical to deploy 3D representation of the physical world such as 3D immersive telepresence,autonomous driving,and cultural heritage preservation.However,point cloud data are distributed irregularly and discontinuously in spatial and temporal domains,where redundant unoccupied voxels and weak correlations in 3D space make achieving efficient compression a challenging problem.In this paper,we propose a spatio-temporal context-guided algorithm for lossless point cloud geometry compression.The proposed scheme starts with dividing the point cloud into sliced layers of unit thickness along the longest axis.Then,it introduces a prediction method where both intraframe and inter-frame point clouds are available,by determining correspondences between adjacent layers and estimating the shortest path using the travelling salesman algorithm.Finally,the few prediction residual is efficiently compressed with optimal context-guided and adaptive fastmode arithmetic coding techniques.Experiments prove that the proposed method can effectively achieve low bit rate lossless compression of point cloud geometric information,and is suitable for 3D point cloud compression applicable to various types of scenes.展开更多
文摘Characterized by geometry and photometry attributes, point cloud has been widely applied in the immersive services of various 3Dobjects and scenes. The development of even more precise capture devices and the increasing requirements for vivid rendering in-evitably induce huge point capacity, thus making the point cloud compression a demanding issue. In this paper, we introduce sev-eral well-known compression algorithms in the research area as well as the boosting industry standardization works. Specifically,based on various applications of this 3D data, we summarize the static and dynamic point cloud compression, both including irreg-ular geometry and photometry information that represent the spatial structure information and corresponding attributes, respective-ly. In the end, we conclude the point cloud compression as a promising topic and discuss trends for future works.
基金This work was supported by National Nature Science Foundation of China(No.61811530281 and 61861136009)Guangdong Regional Joint Foundation(No.2019B1515120076)the Fundamental Research for the Central Universities.
文摘In this paper,a novel compression framework based on 3D point cloud data is proposed for telepresence,which consists of two parts.One is implemented to remove the spatial redundancy,i.e.,a robust Bayesian framework is designed to track the human motion and the 3D point cloud data of the human body is acquired by using the tracking 2D box.The other part is applied to remove the temporal redundancy of the 3D point cloud data.The temporal redundancy between point clouds is removed by using the motion vector,i.e.,the most similar cluster in the previous frame is found for the cluster in the current frame by comparing the cluster feature and the cluster in the current frame is replaced by the motion vector for compressing the current frame.The hrst,the B-SHOT(binary signatures of histograms orientation)descriptor is applied to represent the point feature for matching the corresponding point between two frames.The second,the K-mean algorithm is used to generate the cluster because there are a lot of unsuccessfully matched points in the current frame.The matching operation is exploited to find the corresponding clusters between the point cloud data of two frames.Finally,the cluster information in the current frame is replaced by the motion vector for compressing the current frame and the unsuccessfully matched clusters in the curren t and the motion vectors are transmit ted into the rem ote end.In order to reduce calculation time of the B-SHOT descriptor,we introduce an octree structure into the B-SHOT descriptor.In particular,in order to improve the robustness of the matching operation,we design the cluster feature to estimate the similarity bet ween two clusters.Experimen tai results have shown the bet ter performance of the proposed method due to the lower calculation time and the higher compression ratio.The proposed met hod achieves the compression ratio of 8.42 and the delay time of 1228 ms compared with the compression ratio of 5.99 and the delay time of 2163 ms in the octree-based compression method under conditions of similar distortion rate.
基金supported in part by China Postdoctoral Science Foundation under Grant No.2022M720234in part by the National Natural Science Foundation under Grant Nos.62071449 and U21B2012.
文摘Recent years have witnessed that 3D point cloud compression(PCC)has become a research hotspot both in academia and industry.Especially in industry,the Moving Picture Expert Group(MPEG)has actively initiated the development of PCC standards.One of the adopted frameworks called geometry-based PCC(G-PCC)follows the architecture of coding geometry first and then coding attributes,where the region adaptive hierarchical transform(RAHT)method is introduced for the lossy attribute compression.The upsampled transform domain prediction in RAHT does not sufficiently explore the attribute correlations between neighbor nodes and thus fails to further reduce the attribute redundancy between neighbor nodes.In this paper,we propose a subnode-based prediction method,where the spatial position relationship between neighbor nodes is fully considered and prediction precision is further promoted.We utilize some already-encoded neighbor nodes to facilitate the upsampled transform domain prediction in RAHT by means of a weighted average strategy.Experimental results have illustrated that our proposed attribute compression method shows better rate-distortion(R-D)performance than the latest MPEG G-PCC(both on reference software TMC13-v22.0 and GeS-TM-v2.0).
基金supported by the National Natural Science Foundation of China(No.62001209).
文摘In Video-based Point Cloud Compression(V-PCC),2D videos to be encoded are generated by 3D point cloud projection,and compressed by High Efficiency Video Coding(HEVC).In the process of 2D video compression,the best mode of Coding Unit(CU)is searched by brute-force strategy,which greatly increases the complexity of the encoding process.To address this issue,we first propose a simple and effective Portable Perceptron Network(PPN)-based fast mode decision method for V-PCC under Random Access(RA)configuration.Second,we extract seven simple hand-extracted features for input into the PPN network.Third,we design an adaptive loss function,which can calculate the loss by allocating different weights according to different Rate-Distortion(RD)costs,to train our PPN network.Finally,experimental results show that the proposed method can save encoding complexity of 43.13%with almost no encoding efficiency loss under RA configuration,which is superior to the state-of-the-art methods.The source code is available at https://github.com/Mesks/PPNforV-PCC.
文摘Point cloud compression is critical to deploy 3D representation of the physical world such as 3D immersive telepresence,autonomous driving,and cultural heritage preservation.However,point cloud data are distributed irregularly and discontinuously in spatial and temporal domains,where redundant unoccupied voxels and weak correlations in 3D space make achieving efficient compression a challenging problem.In this paper,we propose a spatio-temporal context-guided algorithm for lossless point cloud geometry compression.The proposed scheme starts with dividing the point cloud into sliced layers of unit thickness along the longest axis.Then,it introduces a prediction method where both intraframe and inter-frame point clouds are available,by determining correspondences between adjacent layers and estimating the shortest path using the travelling salesman algorithm.Finally,the few prediction residual is efficiently compressed with optimal context-guided and adaptive fastmode arithmetic coding techniques.Experiments prove that the proposed method can effectively achieve low bit rate lossless compression of point cloud geometric information,and is suitable for 3D point cloud compression applicable to various types of scenes.
