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U-CAN: Unsupervised Point Cloud Denoising with Consistency-Aware Noise2Noise Matching

arXiv — cs.CV•Thursday, October 30, 2025 at 4:00:00 AM
PositiveArtificial Intelligence
The introduction of U-CAN, an unsupervised framework for point cloud denoising, marks a significant advancement in the field of 3D data processing. By addressing the common issue of noise in point clouds, which can severely hinder tasks like surface reconstruction and shape understanding, U-CAN offers a more efficient solution that reduces the need for extensive manual efforts in training neural networks. This innovation not only enhances the quality of 3D models but also streamlines workflows in various applications, making it a noteworthy development for researchers and practitioners alike.
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