Abstract
We show that denoising of 3D point clouds can be learned unsupervised, directly from noisy 3D point cloud data only. This is achieved by extending recent ideas from learning of unsupervised image denoisers to unstructured 3D point clouds. Unsupervised image denoisers operate under the assumption that a noisy pixel observation is a random realization of a distribution around a clean pixel value, which allows appropriate learning on this distribution to eventually converge to the correct value. Regrettably, this assumption is not valid for unstructured points: 3D point clouds are subject to total noise, i. e., deviations in all coordinates, with no reliable pixel grid. Thus, an observation can be the realization of an entire manifold of clean 3D points, which makes a na\"ive extension of unsupervised image denoisers to 3D point clouds impractical. Overcoming this, we introduce a spatial prior term, that steers converges to the unique closest out of the many possible modes on a manifold. Our results demonstrate unsupervised denoising performance similar to that of supervised learning with clean data when given enough training examples - whereby we do not need any pairs of noisy and clean training data.
Abstract (translated)
我们表明，三维点云的去噪可以在无监督的情况下直接从有噪声的三维点云数据中学习。这是通过将最近的思想从学习无监督图像去噪扩展到非结构化三维点云来实现的。无监督图像去噪器的工作假设是噪声像素观测是围绕干净像素值随机实现分布，这允许对该分布的适当学习最终收敛到正确的值。遗憾的是，这种假设对非结构化点无效：三维点云受到总噪声的影响，即所有坐标的偏差，没有可靠的像素网格。因此，一个观察可以实现一个干净的三维点的整个流形，这使得无监督图像去噪到三维点云的自然扩展不切实际。克服这一点，我们引入了一个空间先验项，该项将转向流形上许多可能模式中最接近的唯一模式。我们的结果表明，在给出足够的训练示例时，无监督的去噪性能类似于有监督的使用干净数据的学习，因此我们不需要任何一对噪音和干净的训练数据。
URL
https://arxiv.org/abs/1904.07615
