Abstract
Existing deep learning-based point cloud denoising methods are generally trained in a supervised manner that requires clean data as ground-truth labels. However, in practice, it is not always feasible to obtain clean point clouds. In this paper, we introduce a novel unsupervised point cloud denoising method that eliminates the need to use clean point clouds as groundtruth labels during training. We demonstrate that it is feasible for neural networks to only take noisy point clouds as input, and learn to approximate and restore their clean versions. In particular, we generate two noise levels for the original point clouds, requiring the second noise level to be twice the amount of the first noise level. With this, we can deduce the relationship between the displacement information that recovers the clean surfaces across the two levels of noise, and thus learn the displacement of each noisy point in order to recover the corresponding clean point. Comprehensive experiments demonstrate that our method achieves outstanding denoising results across various datasets with synthetic and real-world noise, obtaining better performance than previous unsupervised methods and competitive performance to current supervised methods.
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Weijia Wang is currently pursuing his Ph.D. degree at Deakin University. Prior to that, he was awarded Master of Information Technology and Bachelor of Science by the University of Melbourne. His research interests include 3D geometry processing and deep learning.
**ao Liu is currently an associate professor and director for the Software Engineering Innovation Lab at the School of Information Technology, Deakin University. His main research areas include software engineering and distributed/service computing.
Hailing Zhou is an associate professor at Swinburne University of Technology in Australia. Her research areas include multi-modal AI, 3D vision, and vision techniques for robotics.
Lei Wei received his Ph.D. degree in July 2011 from Nanyang Technological University. His research fields include haptic rendering, interaction and collaboration, collision detection, HCI, functionbased and physics-based modelling, as well as web visualisation.
Zhigang Deng is Moores Professor of Computer Science and the director of graduate studies at the Computer Science Department of the University of Houston. His research interests are in the broad areas of computer graphics, computer animation, etc.
Manzur Murshed is currently a professor of computer science in the School of Information Technology at Deakin University. His research interests include video technology, machine learning, big data analytics, and computational mathematics.
Xuequan Lu is a senior lecturer at La Trobe University. He received his Ph.D. degree from the College of Computer Science and Technology at Zhejiang University. His research interests include geometry modelling, processing, and analysis.
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Wang, W., Liu, X., Zhou, H. et al. Noise4Denoise: Leveraging noise for unsupervised point cloud denoising. Comp. Visual Media (2024). https://doi.org/10.1007/s41095-024-0423-3
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DOI: https://doi.org/10.1007/s41095-024-0423-3