Building on recent advances in Bayesian statistics and image denoising, we propose Noise2Score3D, a fully unsupervised framework for point cloud denoising that addresses the critical challenge of limited availability of clean data. Noise2Score3D learns the gradient of the underlying point cloud distribution directly from noisy data, eliminating the need for clean data during training. By leveraging Tweedie's formula, our method performs inference in a single step, avoiding the iterative processes used in existing unsupervised methods, thereby improving both performance and efficiency. Experimental results demonstrate that Noise2Score3D achieves state-of-the-art performance on standard benchmarks, outperforming other unsupervised methods in Chamfer distance and point-to-mesh metrics, and rivaling some supervised approaches. Furthermore, Noise2Score3D demonstrates strong generalization ability beyond training datasets. Additionally, we introduce Total Variation for Point Cloud, a criterion that allows for the estimation of unknown noise parameters, which further enhances the method's versatility and real-world utility.

翻译：基于贝叶斯统计与图像去噪领域的最新进展，我们提出了Noise2Score3D，一种完全无监督的点云去噪框架，旨在解决干净数据可用性有限这一关键挑战。Noise2Score3D直接从含噪数据中学习底层点云分布的梯度，从而在训练过程中无需使用干净数据。通过利用Tweedie公式，我们的方法可在单步内完成推理，避免了现有无监督方法中使用的迭代过程，从而提升了性能与效率。实验结果表明，Noise2Score3D在标准基准测试中达到了最先进的性能，在倒角距离和点到网格度量上优于其他无监督方法，并可媲美部分有监督方法。此外，Noise2Score3D展现出超越训练数据集的强大泛化能力。同时，我们提出了点云全变分准则，该准则能够估计未知的噪声参数，从而进一步增强了本方法的通用性与实际应用价值。