  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.
