Transformers for Object Detection in Large Point Clouds

• URL: http://arxiv.org/abs/2209.15258v1
• Date: Fri, 30 Sep 2022 06:35:43 GMT
• Title: Transformers for Object Detection in Large Point Clouds
• Authors: Felicia Ruppel, Florian Faion, Claudius Gl\"aser, Klaus Dietmayer
• Abstract summary: We present TransLPC, a novel detection model for large point clouds based on a transformer architecture. We propose a novel query refinement technique to improve detection accuracy, while retaining a memory-friendly number of transformer decoder queries. This simple technique has a significant effect on detection accuracy, which is evaluated on the challenging nuScenes dataset on real-world lidar data.
• Score: 9.287964414592826
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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.

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