无需干净数据，一步完成点云去噪，精度效率双提升。
Noise2Score3D: Tweedie's Approach for Unsupervised Point Cloud Denoising
- 直接从噪声数据学习点云分布的得分函数
- 在Chamfer距离和点到网格指标上达最优表现
- 适合真实场景中无干净数据的点云处理
基于贝叶斯统计与图像去噪的最新进展，我们提出Noise2Score3D，一种完全无监督的点云去噪框架。该方法直接从噪声数据中学习点云分布的得分函数，训练过程无需清洁数据。利用Tweedie公式，方法可在单步内完成去噪，避免了现有无监督方法所需的迭代过程，从而提升准确率与效率。此外，我们引入点云总变差（Total Variation for Point Clouds）作为去噪质量评估指标，可估计未知噪声参数。实验表明，Noise2Score3D在标准基准上，在无监督学习方法中于Chamfer距离和点到网格指标上达到领先性能，且具备强泛化能力，超越训练数据集范围。本方法通过解决学习型方法中的泛化难题与缺乏干净数据的问题，为真实场景下的学习型点云去噪提供了可行路径。
原文摘要 · Abstract (English)
Building on recent advances in Bayesian statistics and image denoising, we propose Noise2Score3D, a fully unsupervised framework for point cloud denoising. Noise2Score3D learns the score function of the underlying point cloud distribution directly from noisy data, eliminating the need for clean data during training. Using Tweedie's formula, our method performs denoising in a single step, avoiding the iterative processes used in existing unsupervised methods, thus improving both accuracy and efficiency. Additionally, we introduce Total Variation for Point Clouds as a denoising quality metric, which allows for the estimation of unknown noise parameters. Experimental results demonstrate that Noise2Score3D achieves state-of-the-art performance on standard benchmarks among unsupervised learning methods in Chamfer distance and point-to-mesh metrics. Noise2Score3D also demonstrates strong generalization ability beyond training datasets. Our method, by addressing the generalization issue and challenge of the absence of clean data in learning-based methods, paves the way for learning-based point cloud denoising methods in real-world applications.
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