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 al-so 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,hard-ware 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 ex-plored 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 geo-metric 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(Se-quePU).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.
Three-dimensional point cloudsUpsampling methodsDeep neural networksSelf-supervised learningThree-dimen-sional reconstruction
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