三维点云上采样方法研究综述

Survey of 3D Point Clouds Upsampling Methods

随着深度相机、激光雷达等三维扫描设备的普及,用点云表示三维数据的方法越来越流行,对点云数据的分析与处理也引起了计算机视觉研究领域的极大兴趣。其中,点云上采样任务是一项重要的点云数据处理工作,其结果的好坏关系着下游多种任务的优劣,因此一些研究人员从多个角度深入探索并先后提出了多种点云上采样方法,以期提高计算效率和网络性能,解决点云上采样中的各种难点问题。为了促进之后研究的发展,首先从任务类型角度对现有的点云上采样方法进行了全面的分类与综述,然后对这些点云上采样网络的性能进行了详细的分析与对比,最后针对现存的问题与面临的挑战做了进一步分析,并探索了未来可能的研究方向,希望为三维点云上采样任务未来更深入的研究提供新思路。

With the popularity of three-dimensional(3D)scanning devices such as depth cameras and laser radars,the methods of representing 3D data using point clouds are becoming increasingly popular.The analysis and processing of point cloud data are also arousing great interest in the field of computer visual research.In fact,the quality of the original point clouds directly obtained by sensors is influenced by many factors,such as the self-occlusion of objects themselves,mutual occlusion between objects,differences in scanning accuracy,reflectivity,transparency,as well as environmental limitations during the scanning process,hardware limitations of scanning equipment,inevitably leading to noise,hollow,sparse point clouds.Therefore,obtaining high-quality dense and complete point clouds is an urgent task to be solved.Among them,point cloud upsampling is an important point cloud processing task that aims to transform sparse,non-uniform,and noisy point clouds into dense,uniform,and noiseless point clouds,and the quality of its results affects the quality of various downstream tasks.Therefore,some researchers have further explored and proposed various point cloud upsampling methods from multiple perspectives,so as to improve computing efficiency and network performance,and solve various difficult issues in point cloud upsampling.In order to promote future research on the point cloud upsampling task,first of all,the background and importance of this critical task are introduced.After that,the existing point cloud upsampling methods are comprehensively classified and reviewed from different task type perspectives,including geometric point cloud upsampling(GPU),arbitrary point cloud upsampling(APU),multi-attribute point cloud upsampling(MAPU),multi-modal point cloud upsampling(MMPU),scene point cloud upsampling(ScenePU)and sequential point cloud upsampling(SequePU).Then,the performance of these point cloud upsampling networks is analyzed and compared in detail.Finally,the existing problems and challenges are further analyzed,and possible future research directions are explored,hoping to provide new ideas for further research on 3D point cloud upsampling task and its downstream tasks(such as surface reconstruction)in the future.