摘要
随着科学技术的快速发展,三维点云采集设备的精度不断提升,海量三维点云数据的获取成为可能。然而,三维点云数据点分布不规则、数量巨大等特性给存储和传输带来了巨大的挑战,点云编码势在必行。针对三维点云几何信息,文中从数据采样的角度出发,将三维点云编码问题转换为三维点云采样-重建问题,并提出基于采样的三维点云几何编码框架。该框架首先在编码端使用指定采样率的三维点云下采样方法,将原始三维点云下采样至指定点数的稀疏三维点云,然后采用任意现有的编码方法对稀疏三维点云进行编码(待编码点数大幅减少,可有效降低编码码率),最后使用三维点云上采样方法将解码端获得的稀疏三维点云插值重建成与原始输入点云外形近似的高质量稠密三维点云。实验结果表明,与MPEG提出的最新G-PCC三维点云编码标准相比,在相同码率下,文中提出的三维点云几何编码框架可以使解码端重建三维点云的客观质量平均提升5.49 dB,同时呈现出更好的主观视觉效果。
With the development of science and technology,the accuracy of 3D point cloud acquisition equipment has been continuously improved,and the acquisition of massive 3D point cloud data has become a reality.However,the irregular distribution and huge number of data points of 3D point cloud bring great challenges to data storage and transmission.Therefore,3D point cloud coding is imperative.From the perspective of data sampling,this paper transforms the 3D point cloud coding problem into a 3D point cloud sampling-reconstruction problem,and proposes a sampling-based 3D point cloud geometry coding framework.In this framework,firstly,the down-sampling method is used to sample the original 3D point cloud to the sparse 3D point cloud with a specified number of points.Then,the sparse 3D point cloud is encoded using any existing coding methods(the number of encoding points is significantly reduced,which can effectively reduce the encoding rate).Finally,by using the proposed upsampling method,the decoded sparse 3D point cloud is interpolated as a high-quality dense 3D point cloud similar to the shape of the original input point cloud.Experimental results show that,as compared with the latest G-PCC provided by MPEG,the proposed 3D point cloud geometry coding framework improves the objective quality of the reconstructed 3D point cloud by 5.49 dB on average,and presents better subjective visual effect.
作者
刘昊
元辉
陈晨
高伟
LIU Hao;YUAN Hui;CHEN Chen;GAO Wei(School of Control Science and Engineering,Shandong University,Jinan 250061,Shandong,China;School of Computer and Control Engineering,Yantai University,Yantai 264005,Shandong,China;Peking University Shenzhen Graduate School,Shenzhen 518055,Guangdong,China)
出处
《华南理工大学学报（自然科学版）》
EI
CAS
CSCD
北大核心
2024年第6期148-156,共9页
Journal of South China University of Technology(Natural Science Edition)
基金
国家自然科学基金资助项目(62172259,61871342)
山东省自然科学基金资助项目(ZR2023QF111)
香港RGC资助项目(CityU11202320)
OPPO科研基金资助项目。
关键词
点云
几何编码
深度学习
采样
point cloud
geometry coding
deep learning
sampling
