Arbitrary-Scale Point Cloud Upsampling via Enhanced Geometric Spatial Consistency

Point cloud upsampling is an essential yet challenging task in various 3D computer vision and graphics applications. Existing methods often struggle with limitations such as the generation of outliers or shrinkage artifacts. Additionally, these methods usually ignore the overall spatial structure of point clouds, leading to suboptimal results. To tackle these challenges, we propose a novel framework that enhances geometric spatial consistency in upsampled point clouds through a dual-supervision mechanism and enables the generation of high-fidelity results with precise geometric structures. Specifically, we first design a tailored feature extractor that iteratively extracts the comprehensive and distinctive features by integrating both fine-grained local geometric details and global structure information. Then, our network predicts the point-to-point distances and Chamfer distances of upsampled points to accurately capture the spatial relation within them. To enhance spatial consistency, we formulate a joint loss function that enables our model to perceive the spatial relations between points by indirect and direct supervision. This ensures the precise alignment between upsampled points and ground truth during training. Furthermore, we propose a coordinate reconstruction to generate more high-quality upsampled points iteratively. We conduct extensive experiments across multiple benchmark datasets and downstream tasks. The results comprehensively demonstrate that our method achieves state-of-the-art performance and exhibits superior generalisation capabilities.