PTANet: Triple Attention Network for point cloud semantic segmentation

For 3D point cloud semantic segmentation, mining more informative features to enrich contextual representation is regarded as the key to achieve better segmentation performance. Unfortunately, the existing point cloud segmentation network lacks a comprehensive consideration of utilizing contextual information from both global and local perspectives, thus failing to fully explore the contextual representation, which prevents fine-grained objects from being accurately recognized. Therefore, this paper proposes a neural network dubbed PTANet that effectively enriches the contextual representation to improve segmentation accuracy. PTANet possesses two uncomplicated and effective parts: Triple Attention Block and Density Scale Learning Strategy. Triple Attention Block consists of three sub modules: 1. Position attention module updates feature maps by modeling the interdependency between the spatial positions of each point. 2. Channel attention module recalibrates the original feature in the light of the correlation weight between the channels of feature maps to enrich the contextual representation globally. 3. Local Region attention module calculates the interdependence weight between local neighbors to further complement the local feature information. In addition, to alleviate the adverse effect of non-uniform distribution of point cloud on the inference results, Density Scale Learning Strategy applies the kernel density estimation under the adaptive bandwidth to fit the density scale of each point. In particular, the density scale weighted to the feature maps can also supplement the density information for local features. The experimental performance verifies the effectiveness of PTANet. It obtained 86.1% mIoU on ShapeNet, 62.4% mIoU on ScannetV2, and 87.9% OA on S3DIS.