杭州师范大学学报（自然科学版）2025，Vol.24Issue(6)：664-672,9.DOI:10.19926/j.cnki.issn.1674-232X.2023.09.151
基于multi-branch的点云几何后处理方法
Point Cloud Geometry Post-processing Method Based on Multi-branch
钱虞杰 1丁丹丹1
作者信息
- 1. 杭州师范大学信息科学与技术学院,浙江 杭州 311121
- 折叠
摘要
Abstract
Geometry-based point cloud compression(G-PCC)effectively reduces the bandwidth and storage requirements for point cloud transmission.However,the quality of the reconstructed point cloud often degrades significantly due to point disappearance.To address this issue,this paper proposed a multi-branch-based geometry post-processing method for G-PCC compressed point cloud.The method extracted multi-scale geometric features and employed a k-nearest neighbors(kNN)based max pooling layer at each scale to aggregate geometric neighborhood information,thereby predicting voxel occupancy probabilities for more accurate point cloud reconstruction.Evaluated under the Moving Picture Experts Group(MPEG)common test conditions,the proposed method achieved average BD-Rate gains of 91.89％(84.57％)and 75.24％(73.51％)for D1(D2)metrics compared to G-PCC(octree)and G-PCC(trisoup),respectively.It also obtained average BD-Rate gains of 76.78％(70.37％)for Dl(D2)over the traditional LUT method and 23.95％(21.41％)over the deep learning-based DGPP method.Furthermore,the proposed approach demonstrated lower computational complexity compared to existing learning-based methods,indicating promising potential for practical applications.关键词
点云几何压缩/后处理/深度学习/多尺度/k近邻算法Key words
point cloud geometry compression/post-processing/deep learning/multi-scale/k-nearest neighbors分类
信息技术与安全科学引用本文复制引用
钱虞杰,丁丹丹..基于multi-branch的点云几何后处理方法[J].杭州师范大学学报（自然科学版）,2025,24(6):664-672,9.
