Point Cloud Color Upsampling with Attention-Based Coarse Colorization and Refinement

Point cloud color upsampling is an important and less explored research topic. State-of-the-art methods colorize points based on the colors of neighboring points and geometric distances. However, these methods often suffer from blurring and noise at color boundaries since object textures can have large color variations even between geometrically neighboring positions. In this paper, we propose a point cloud color upsampling method with attention weights for neighboring points. The proposed method first performs coarse colorization with the colors of low-resolution points neighboring the high-resolution points and predicted weights. Then, it refines the colors by predicting offsets for high-resolution points with aggregate features obtained from the low-resolution points. Both quantitative and qualitative experimental results on datasets acquired in real-world environments demonstrate that the proposed method achieves significantly superior color upsampling performance compared to state-of-the-art methods.