端到端优化的3D点云几何信息有损压缩模型  

End-to-End Optimized 3D Point Cloud Geometric Information Lossy Compression Model

作　　者：徐嘉诚 方志军[1] 黄勃[1] 高永彬 周恒 XU Jiacheng;FANG Zhijun;HUANG Bo;GAO Yongbin;ZHOU Heng(School of Electronic and Electrical Engineering,Shanghai University of Engineering Science,Shanghai 201620,China)

机构地区：[1]上海工程技术大学,电子电气工程学院,上海201620

出　　处：《武汉大学学报（理学版）》2022年第3期297-303,共7页Journal of Wuhan University:Natural Science Edition

基　　金：国家自然科学基金(61772328,61831018)。

摘　　要：3D点云在虚拟现实与增强现实领域有着广泛应用。复杂的三维场景往往需要大量的点云来表示,并且需要大量的空间来存储。提出端到端优化的3D点云几何信息有损压缩模型,该模型利用自编码器和深度卷积生成对抗网络算法解决了大面积点云缺失问题,并重建出高质量的点云数据。为了在提高点云重建质量的同时,保证压缩效率不降低,提出反卷积跳跃连接结构。该结构将解码器各层信息传递到最后输出层进行特征融合,最终解码器能够在更低的码率下重建出高质量的点云。实验表明,在MVUB数据集上与MPEG G-PCC(Octree)标准相比,本文模型使BD-BR平均降低62.01%,BD-PSNR平均提高4.4971 dB,并获得了更高的视觉质量。3D point cloud has been widely applied in virtual reality and augmented reality.A complex 3D scene always needs a large number of point cloud to represent and demands a lot of space to store.Thus,point cloud compression becomes a crucial issue to research.An end-to-end optimized lossy geometry compression model for 3D point clouds geometric information is proposed.The model solves the problem of missing large area point cloud by using autoencoder and deep convolution generative adversarial network algorithm and reconstructs high-quality point cloud data.In order to improve the reconstruction quality of the high point cloud and ensure that the compression efficiency does not decrease,a deconvolution jump connection structure is proposed in this paper.This structure transmits the information of each layer of the decoder to the last output layer for feature fusion,and finally,the decoder can reconstruct a highquality point cloud at lower bits rate.Experimental results show that compared with MPEG G-PCC(Octree)standard,the proposed model can decrease BD-BR by 62.01%and increase BD-PSNR by 4.4971 dB on the MVUB dataset,and obtain higher visual quality.

关 键 词：点云 几何压缩 自编码器 生成对抗网络 

分 类 号：TP391.41[自动化与计算机技术—计算机应用技术]