Boundary information plays a significant role in 2D image segmentation, while usually being ignored in 3D point cloud segmentation where ambiguous features might be generated in feature extraction, leading to misclassification in the transition area between two objects. In this paper, firstly, we propose a Boundary Prediction Module (BPM) to predict boundary points. Based on the predicted boundary, a boundary-aware Geometric Encoding Module (GEM) is designed to encode geometric information and aggregate features with discrimination in a neighborhood, so that the local features belonging to different categories will not be polluted by each other. To provide extra geometric information for boundary-aware GEM, we also propose a light-weight Geometric Convolution Operation (GCO), making the extracted features more distinguishing. Built upon the boundary-aware GEM, we build our network and test it on benchmarks like ScanNet v2, S3DIS. Results show our methods can significantly improve the baseline and achieve state-of-the-art performance. Code is available at https://github.com/JchenXu/BoundaryAwareGEM.

本文提供了一种新的方法，通过Boundary Prediction Module（BPM）和Boundary-aware Geometric Encoding Module（GEM）对二维和三维图像分割，对边界信息进行处理，在局部区域中对信息进行编码，从而避免相同类别的局部特征互相污染，并且引入了light-weight Geometric Convolution Operation (GCO)来提供更多几何信息，实验证明其性能达到了最先进水平。

面向点云语义分割的边界感知几何编码