Enhance Local Feature Consistency with Structure Similarity Loss for 3D Semantic Segmentation

Cheng-Wei Lin, Fang-Yu Syu, Yi-Ju Pan, Kuan-Wen Chen

Abstract: Recently, many research studies have been carried out on using deep learning methods for 3D point cloud understanding. However, there is still no remarkable result on 3D point cloud semantic segmentation compared to those of 2D research. One important reason is that 3D data has higher dimensionality but lacks large datasets, which means that the deep learning model is difficult to optimize and easy to overfit. To overcome this, an essential method is to provide more priors to the learning of deep models. In this paper, we focus on semantic segmentation for point clouds in the real world. To provide priors to the model, we propose a novel loss function called Linearity and Planarity to enhance local feature consistency in the regions with similar local structure. Experiments show that the proposed method improves baseline performance on both indoor and outdoor datasets e.g. S3DIS and Semantic3D.