PU-GSM: A Latent Geometry-Guided Self-Similarity Model for Point Cloud Upsampling

This is the authors' abstract. We don't add key points for this paper.

Existing point cloud upsampling methods typically treat upsampling as a local interpolation problem, neglecting the importance of global correlations within point sets, which can limit their performance. To address this limitation, we exploit the inherent self-similarity of point clouds from a global perspective and propose PU-GSM, a latent geometry-guided self-similarity model for upsampling. We first generate a lower-resolution sparse sub-point cloud (SPC) by downsampling the input point cloud (IPC). Then, we introduce a latent geometry-guided self-similarity model (LGSM) that learns a point distribution on the underlying surface of SPC by exploiting the inherent self-similarity of IPC. Next, we reuse the LGSM for the remaining points (i.e., the points left after removing SPC from IPC). Afterward, we introduce a gradient-aware dual domain refiner to generate and calibrate the upsampled point cloud from the learned point distribution. Finally, we propose an inference-free latent vector matching approach to regularize the upsampled point cloud by enhancing the feature similarity between the upsampled point cloud and the ground truth in latent space. Extensive experiments show that PU-GSM achieves better upsampling results compared to state-of-the-art methods. Our code will be available at: https://github.com/liuhaoyun/PU-GSM.