论文标题
学习点云降解的图形横线表示
Learning Graph-Convolutional Representations for Point Cloud Denoising
论文作者
论文摘要
点云是一种越来越相关的数据类型，但它们通常被噪声损坏。我们提出了一个基于图形横向倾斜层的深度神经网络，可以优雅地处理通过基于学习的点云处理方法遇到的置换不变性问题。该网络是完全跨的，可以通过在点的高维特征表示之间的相似性中动态构造邻域图来构建功能的复杂层次结构。当加上损失促进与理想表面的距离的损失时，所提出的方法在各种指标上都显着优于最先进的方法。特别是，它能够从倒角测量和表面正常的质量方面进行改进，从而可以从DeNo的数据中估算出来。我们还表明，在高噪声水平和存在结构化噪声（例如在实际发光量扫描中遇到的噪声）下，它在高噪声水平上尤其强大。
Point clouds are an increasingly relevant data type but they are often corrupted by noise. We propose a deep neural network based on graph-convolutional layers that can elegantly deal with the permutation-invariance problem encountered by learning-based point cloud processing methods. The network is fully-convolutional and can build complex hierarchies of features by dynamically constructing neighborhood graphs from similarity among the high-dimensional feature representations of the points. When coupled with a loss promoting proximity to the ideal surface, the proposed approach significantly outperforms state-of-the-art methods on a variety of metrics. In particular, it is able to improve in terms of Chamfer measure and of quality of the surface normals that can be estimated from the denoised data. We also show that it is especially robust both at high noise levels and in presence of structured noise such as the one encountered in real LiDAR scans.
