AI RESEARCH
UGD: An Unsupervised Geometric Distance for Evaluating Real-world Noisy Point Cloud Denoising
arXiv CS.CV
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ArXi:2604.16976v1 Announce Type: new Point cloud denoising is a fundamental and crucial challenge in real-world point cloud applications. Existing quantitative evaluation metrics for point cloud denoising methods are implemented in a supervised manner, which requires both the denoised point cloud and the corresponding ground-truth clean point cloud to compute a representative geometric distance. This requirement is highly problematic in real-world scenarios, where ground-truth clean point clouds are often unavailable.