Fast 3D Semantic Segmentation Using a Self Attention Network and Random Sampling

For many use cases, reliable autonomous behavior of mobile robots can only be achieved if semantic information about the environment is available together with a topological map. However, current techniques either rely on costly sampling methods or involve computationally heavy pre- or post-processing steps, making them unsuitable for real-time systems with limited resources. In this paper, we propose an optimized approach for 3D point cloud processing that uses a self attention network combined with random sampling to directly infer the semantics of individual 3D points. The approach achieves competitive results on large scale point cloud data sets, including Semantic KITTI and S3DIS.