Scale Point Cloud Upsampling

Scale point cloud upsampling aims to generate high-density point clouds from sparse input, improving the quality of 3D data for various applications. Recent research focuses on developing methods that can handle arbitrary upsampling scales within a single model, moving beyond fixed-rate approaches. This is achieved through techniques like voxel-based networks, implicit neural representations, and iterative optimization guided by learned distance functions, all striving for improved accuracy and efficiency in generating dense, uniform, and geometrically consistent point clouds. The resulting higher-quality point clouds have significant implications for 3D computer vision, robotics, and other fields reliant on accurate 3D data representation.