GraphDAE-PU: Graph Denosing Auto-Encoder for Arbitrary-Scale Point Cloud Upsampling

Abstract

Existing learning-based arbitrary-scale point cloud upsampling methods are usually challenged with limited point cloud feature representation and noise-sensitive refinement of coarse point cloud. In this paper, we introduce GraphDAE-PU, a novel framework for point cloud upsampling that addresses the challenges associated with arbitrary-scale point cloud upsampling. Our approach integrates graph representation with a denoising auto-encoder architecture, harnessing the intrinsic structure of point clouds to enhance feature extraction and refine the upsampled points. Specifically, the use of a graph neural network enables our model to capture complex geometric relationships within the data, thereby improving the accuracy of the generated point clouds. Concurrently, the denoising auto-encoder refines coarse, midpoint-interpolated point clouds, resulting in surfaces that are not only accurate and uniform but also robust to noise. Experimental evaluations, both qualitative and quantitative, reveal that GraphDAE-PU consistently outperforms existing methods, demonstrating its accuracy and robustness in arbitrary-scale upsampling tasks.