无需配对数据，通过一致性感知的噪声到噪声匹配实现点云去噪。
U-CAN: Unsupervised Point Cloud Denoising with Consistency-Aware Noise2Noise Matching
- 利用多步去噪路径与噪声到噪声匹配学习去噪规律。
- 在多个基准上超越现有无监督方法，媲美有监督模型性能。
- 提出几何一致性约束，适用于3D点云和2D图像去噪任务。
扫描传感器获取的点云常受噪声干扰，严重影响后续任务（如表面重建和形状理解）。以往方法依赖大量人工标注的带噪-干净点云对训练神经网络，成本高昂。本文提出U-CAN框架，一种基于一致性感知噪声到噪声匹配的无监督点云去噪方法。通过神经网络为每个点生成多步去噪路径，利用新型损失函数实现对多个带噪观测的统计推理。进一步引入去噪几何一致性约束，学习具有鲁棒性的去噪模式。该约束具有通用性，不仅适用于3D点云，也可提升2D图像去噪效果。在点云去噪、上采样及图像去噪的广泛基准测试中，U-CAN显著优于现有无监督方法，且性能可比肩有监督方法。
原文摘要 · Abstract (English)
Point clouds captured by scanning sensors are often perturbed by noise, which have a highly negative impact on downstream tasks (e.g. surface reconstruction and shape understanding). Previous works mostly focus on training neural networks with noisy-clean point cloud pairs for learning denoising priors, which requires extensively manual efforts. In this work, we introduce U-CAN, an Unsupervised framework for point cloud denoising with Consistency-Aware Noise2Noise matching. Specifically, we leverage a neural network to infer a multi-step denoising path for each point of a shape or scene with a noise to noise matching scheme. We achieve this by a novel loss which enables statistical reasoning on multiple noisy point cloud observations. We further introduce a novel constraint on the denoised geometry consistency for learning consistency-aware denoising patterns. We justify that the proposed constraint is a general term which is not limited to 3D domain and can also contribute to the area of 2D image denoising. Our evaluations under the widely used benchmarks in point cloud denoising, upsampling and image denoising show significant improvement over the state-of-the-art unsupervised methods, where U-CAN also produces comparable results with the supervised methods.
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