Name
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. |
Year | DOI | Venue |
|---|---|---|
2022 | 10.1109/ITSC55140.2022.9921840 | International Conference on Intelligent Transportation Systems (ITSC) |
DocType | Citations | PageRank |
Conference | 0 | 0.34 |
References | Authors | |
0 | 4 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Felicia Ruppel | 1 | 0 | 0.34 |
| Florian Faion | 2 | 74 | 7.95 |
| Claudius Gläser | 3 | 17 | 4.64 |
| Klaus Dietmayer | 4 | 822 | 102.64 |