通过镜像点一致性实现无监督点云去噪，定位原始表面更精准。
SIMPC: Learning Self-Induced Mirror-Point Consistency for Unsupervised Point Cloud Denoising
- 为每个噪声点生成对称镜像点，利用几何先验引导去噪。
- 在合成与真实数据集上超越当前最优无监督方法，媲美部分有监督模型。
- 适合需要无标注点云去噪的工业应用，如三维扫描重建。
点云中的噪声会直接扰动编码空间位置与几何信息的点坐标，使得一一对应关系构建比图像更困难。现有方法通过噪声或最优传输施加统计映射，但存在对应模糊问题。本文提出自诱导镜像点一致性（SIMPC），以无监督方式学习点与潜在表面之间的确定性对应关系。针对每个噪声点，SIMPC在其所处表面相反侧生成一个镜像点，并在去噪过程中借助几何先验进行引导。通过强制原始点与其镜像点的去噪目标保持一致，SIMPC有效定位了潜在表面位置。在合成与真实世界数据集上的大量实验表明，SIMPC显著优于当前最先进的无监督方法，并超越多个强监督基线。
原文摘要 · Abstract (English)
In point clouds, noise directly perturbs point coordinates that encode both spatial location and geometry, making one-to-one correspondence construction more challenging than in images. Existing methods impose statistical mappings across noisy variants via noise or optimal transport, but suffer from correspondence ambiguity. In this work, we propose Self-Induced Mirror-Point Consistency (SIMPC) to learn deterministic correspondences between points and the underlying surface in an unsupervised manner. For each noisy point, SIMPC generates a mirror-point on the opposite side of the underlying surface, guided by geometric priors during the denoising process. By encouraging consistency between the denoising targets of the original point and its mirror counterpart, SIMPC effectively localizes the position of underlying surface. Extensive experiments on synthetic and real-world datasets demonstrate that SIMPC significantly outperforms state-of-the-art unsupervised methods and surpasses several strong supervised counterparts.
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