Transformers for Object Detection in Large Point Clouds

• Published in: 2022 IEEE 25th International Conference on Intelligent Transportation Systems (ITSC) pp. 832 - 838
• Main Authors: Ruppel, Felicia, Faion, Florian, Glaser, Claudius, Dietmayer, Klaus
• Format: Conference Proceeding
• Language: English
• Published: IEEE 08.10.2022
• Subjects: Decoding; Laser radar; Object detection; Point cloud compression; Radar tracking; Task analysis; Transformers
• DOI: 10.1109/ITSC55140.2022.9921840

We present TransLPC, a novel detection model for large point clouds that is based on a transformer architecture. While object detection with transformers has been an active field of research, it has proved difficult to apply such models to point clouds that span a large area, e.g. those that are common in autonomous driving, with lidar or radar data. TransLPC is able to remedy these issues: The structure of the transformer model is modified to allow for larger input sequence lengths, which are sufficient for large point clouds. Besides this, we propose a novel query refinement technique to improve detection accuracy, while retaining a memory-friendly number of transformer decoder queries. The queries are repositioned between layers, moving them closer to the bounding box they are estimating, in an efficient manner. This simple technique has a significant effect on detection accuracy, which is evaluated on the challenging nuScenes dataset on real-world lidar data. Besides this, the proposed method is compatible with existing transformer-based solutions that require object detection, e.g. for joint multi-object tracking and detection, and enables them to be used in conjunction with large point clouds.