Contrastive Learning for Joint Normal Estimation and Point Cloud Filtering
This addresses the challenge of noise sensitivity and feature preservation in 3D point cloud processing for applications like computer vision and robotics, representing a novel integration rather than an incremental improvement.
The paper tackles the joint problem of point cloud filtering and normal estimation by proposing a deep learning method that uses contrastive learning with noise augmentation and a joint loss, achieving state-of-the-art performance in preserving sharp features and fine details.
Point cloud filtering and normal estimation are two fundamental research problems in the 3D field. Existing methods usually perform normal estimation and filtering separately and often show sensitivity to noise and/or inability to preserve sharp geometric features such as corners and edges. In this paper, we propose a novel deep learning method to jointly estimate normals and filter point clouds. We first introduce a 3D patch based contrastive learning framework, with noise corruption as an augmentation, to train a feature encoder capable of generating faithful representations of point cloud patches while remaining robust to noise. These representations are consumed by a simple regression network and supervised by a novel joint loss, simultaneously estimating point normals and displacements that are used to filter the patch centers. Experimental results show that our method well supports the two tasks simultaneously and preserves sharp features and fine details. It generally outperforms state-of-the-art techniques on both tasks. Our source code is available at https://github.com/ddsediri/CLJNEPCF.