Robust graph-based segmentation of noisy point clouds

Point clouds are commonly used in a variety of applications such as telepresence, robotics, autonomous driving, etc. However, point clouds are often corrupted by noise, which hinders the performance of point cloud analysis such as segmentation. In this paper, we present a novel approach for robust segmentation of noisy point clouds over graphs, leveraging on graph signal processing. To handle the noise in the input point cloud, we design a feature denoising module that removes the noise from the feature representations, which benefits downstream tasks with the denoised features. In addition, we propose an end-to-end framework that integrates denoising and segmentation tasks for input noisy point clouds, which are jointly optimized by minimizing the segmentation loss and the proposed graph-based denoising loss. We evaluate our method on noisy point clouds and demonstrate the superiority and robustness of the proposed method.