V-PCC: performance evaluation of the first MPEG Point Cloud Codec

In this article, we present an efficient coding scheme for LiDAR point cloud maps. As a point cloud map consists of numerous single scans spliced together, by recording the time stamp and quaternion matrix of each scan during map building, we cast the point cloud map compression into the point cloud sequence compression problem. The coding architecture includes two techniques: intra-coding and inter-coding. For intra-frames, a segmentation-based intra-prediction technique is developed. For inter-frames, an interpolation-based inter-frame coding network is explored to remove temporal redundancy by generating virtual point clouds based on the decoded frames. We only need to code the difference between the original LiDAR data and the intra/inter-predicted point cloud data. The point cloud map can be reconstructed according to the decoded point cloud sequence and quaternion matrices. Experiments on the KITTI dataset show that the proposed coding scheme can largely eliminate the temporal and spatial redundancies. The point cloud map can be encoded to 1/24 of its original size with 2 mm-level precision. Our algorithm also obtains better coding performance compared with the octree and Google Draco algorithms.

Section: Resultsmentioning

confidence: 99%

Section: Point Cloud Coding: a Brief Reviewmentioning

confidence: 99%

Section: Point Cloud Coding: a Brief Reviewmentioning

confidence: 99%

“…

curves of our method in compared to Google Draco [ 28 ], MPEG TMC13 [ 20 ] and Tu’s method [ 26 ]: ( a ) campus , ( b ) city , ( c ) road , and ( d ) residential . …”

Section: Figurementioning

confidence: 99%

See 2 more Smart Citations

An Efficient LiDAR Point Cloud Map Coding Scheme Based on Segmentation and Frame-Inserting Network

Wang

Jiang

Sun

et al. 2022

“…The MPEG-3DG working group divided point-cloud compression standards into video-based point-cloud compression (V-PCC) and geometry-based point-cloud compression (G-PCC) according to processing methods. V-PCC aims to provide low-complexity decoding capability for applications requiring real-time decoding, such as virtual/augmented reality and immersive communication [ 14 ]. G-PCC is believed to provide efficient lossless and lossy compression for the deployment of autonomous driving, 3D maps, and other applications using point clouds generated by radar [ 15 ].…”

Section: Related Workmentioning

confidence: 99%

An Optimized Convolutional Neural Network for the 3D Point-Cloud Compression

Luo

Xiong

et al. 2023

Due to the tremendous volume taken by the 3D point-cloud models, knowing how to achieve the balance between a high compression ratio, a low distortion rate, and computing cost in point-cloud compression is a significant issue in the field of virtual reality (VR). Convolutional neural networks have been used in numerous point-cloud compression research approaches during the past few years in an effort to progress the research state. In this work, we have evaluated the effects of different network parameters, including neural network depth, stride, and activation function on point-cloud compression, resulting in an optimized convolutional neural network for compression. We first have analyzed earlier research on point-cloud compression based on convolutional neural networks before designing our own convolutional neural network. Then, we have modified our model parameters using the experimental data to further enhance the effect of point-cloud compression. Based on the experimental results, we have found that the neural network with the 4 layers and 2 strides parameter configuration using the Sigmoid activation function outperforms the default configuration by 208% in terms of the compression-distortion rate. The experimental results show that our findings are effective and universal and make a great contribution to the research of point-cloud compression using convolutional neural networks.

Subjective Quality Assessment of V-PCC-Compressed Dynamic Point Clouds Degraded by Packet Losses

Dumic

Cruz

2023

This article describes an empirical exploration on the effect of information loss affecting compressed representations of dynamic point clouds on the subjective quality of the reconstructed point clouds. The study involved compressing a set of test dynamic point clouds using the MPEG V-PCC (Video-based Point Cloud Compression) codec at 5 different levels of compression and applying simulated packet losses with three packet loss rates (0.5%, 1% and 2%) to the V-PCC sub-bitstreams prior to decoding and reconstructing the dynamic point clouds. The recovered dynamic point clouds qualities were then assessed by human observers in experiments conducted at two research laboratories in Croatia and Portugal, to collect MOS (Mean Opinion Score) values. These scores were subject to a set of statistical analyses to measure the degree of correlation of the data from the two laboratories, as well as the degree of correlation between the MOS values and a selection of objective quality measures, while taking into account compression level and packet loss rates. The subjective quality measures considered, all of the full-reference type, included point cloud specific measures, as well as others adapted from image and video quality measures. In the case of image-based quality measures, FSIM (Feature Similarity index), MSE (Mean Squared Error), and SSIM (Structural Similarity index) yielded the highest correlation with subjective scores in both laboratories, while PCQM (Point Cloud Quality Metric) showed the highest correlation among all point cloud-specific objective measures. The study showed that even 0.5% packet loss rates reduce the decoded point clouds subjective quality by more than 1 to 1.5 MOS scale units, pointing out the need to adequately protect the bitstreams against losses. The results also showed that the degradations in V-PCC occupancy and geometry sub-bitstreams have significantly higher (negative) impact on decoded point cloud subjective quality than degradations of the attribute sub-bitstream.