Unsupervised arbitrary-scale point cloud upsampling by learning neural gradient function Unsupervised arbitrary-scale point cloud upsampling by learning neural gradient function

• Published in: Multimedia systems Vol. 31; no. 4
• Main Authors: Gao, Tao, Feng, Jiangshan, Wu, Xiaoqun, Li, Haisheng, Wang, Xiaochuan
• Format: Journal Article
• Language: English
• Published: Berlin/Heidelberg Springer Berlin Heidelberg 01.08.2025; Springer Nature B.V
• Subjects: Computer Communication Networks; Computer Graphics; Computer Science; Cryptology; Data Storage Representation; Image segmentation; Multimedia Information Systems; Operating Systems; Regular Paper; Semantic segmentation; Point cloud upsampling; Density-sensitive; Neural gradient function; Arbitrary scale; Unsupervised learning

Point cloud upsampling aims to generate a dense and uniform point cloud from a sparse input, supporting various downstream tasks such as surface reconstruction and semantic segmentation. Current point cloud upsampling approaches mainly rely on ground truth complete point clouds as supervision, which...