Neural Visibility of Point Sets

Point clouds are widely used representations of 3D data, but determining the visibility of points from a given viewpoint remains a challenging problem due to their sparse nature and lack of explicit connectivity. Traditional methods, such as Hidden Point Removal (HPR), face limitations in computational efficiency, robustness to noise, and handling concave regions or low-density point clouds. In this paper, we propose a novel approach to visibility determination in point clouds by formulating it as a binary classification task. The core of our network consists of a 3D U-Net that extracts view-independent point-wise features and a shared multi-layer perceptron (MLP) that predicts point visibility using the extracted features and view direction as inputs. The network is trained end-to-end with ground-truth visibility labels generated from rendered 3D models. Our method significantly outperforms HPR in both accuracy and computational efficiency, achieving up to 126 times speedup on large point clouds. Additionally, our network demonstrates robustness to noise and varying point cloud densities and generalizes well to unseen shapes. We validate the effectiveness of our approach through extensive experiments on the ShapeNet, ABC Dataset and real-world datasets, showing substantial improvements in visibility accuracy. We also demonstrate the versatility of our method in various applications, including point cloud visualization, surface reconstruction, normal estimation, shadow rendering, and viewpoint optimization. Our code and models are available at https://github.com/octree-nn/neural-visibility.