PU-SDF: Arbitrary-Scale Uniformly Upsampling Point Clouds via Signed Distance Functions

Point cloud upsampling is a crucial technique for improving the performance of 3D data understanding, enabling the creation of dense and uniform point clouds from raw and sparse input data. However, most existing methods exhibit limitations in dealing with different scaling factors, either requiring multiple networks or producing unevenly distributed points. To tackle this issue, this paper proposes a deep learning method called PU-SDF, which consists of a local-feature-based signed distance function network (LSDF-network) and a 3D-grid query points generation module (GPGM). The proposed LSDF-network can perform point cloud upsampling at arbitrary rates and can be easily adapted to handle unseen datasets. Additionally, we propose the GPGM that generates uniform and unlimited query points in sparse voxel space with arbitrary resolution. Extensive qualitative and quantitative evaluations demonstrate the superior performance of the PU-SDF method, achieving state-of-the-art point cloud upsampling performance.