2022/03/29 by Peer Schütt, Radu Alexandru Roşu, Schütt, Peer +4 · 1 citation
Computer Science · Earth and Planetary Sciences · Engineering · Environmental Science · #3D Modeling in Geospatial Applications #3D Surveying and Cultural Heritage #Computer Vision and Pattern Recognition (cs.CV) #FOS: Computer and information sciences #Machine Learning (cs.LG) #Remote Sensing and LiDAR Applications #cs.CV #cs.LG
paper · pdf · doi:10.48550/arxiv.2203.15469
Accepted IEEE International Conference on Robotics and Automation (ICRA) 2022, Code available at https://github.com/AIS-Bonn/temporal_latticenet
arxiv created 2022/03/29 · openalex publication_date 2022/03/29 · arxiv updated 2022/03/30 · openalex created_date 2022/04/27 · openalex updated_date 2026/07/28
Semantic segmentation is a core ability required by autonomous agents, as being able to distinguish which parts of the scene belong to which object class is crucial for navigation and interaction with the environment. Approaches which use only one time-step of data cannot distinguish between moving objects nor can they benefit from temporal integration. In this work, we extend a backbone LatticeNet to process temporal point cloud data. Additionally, we take inspiration from optical flow methods and propose a new module called Abstract Flow which allows the network to match parts of the scene with similar abstract features and gather the information temporally. We obtain state-of-the-art results on the SemanticKITTI dataset that contains LiDAR scans from real urban environments. We share the PyTorch implementation of TemporalLatticeNet at https://github.com/AIS-Bonn/temporallatticenet .