Transformers for Object Detection in Large Point Clouds

Felicia, Ruppel,; Faion, Florian; Gläser, Claudius; Dietmayer, Klaus

doi:10.1109/itsc55140.2022.9921840

Cited by 4 publications

(1 citation statement)

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“…Between decoder layers, the queries' anchor locations are refined to move them closer to the object they are aiming to estimate, following [21], [26]. With group regression, this poses the question which of the six estimated object locations per query to use for refinement.…”

Section: Query Refinementmentioning

confidence: 99%

Transformers for Multi-Object Tracking on Point Clouds

Felicia

Faion

Gläser

et al. 2022

2022 IEEE Intelligent Vehicles Symposium (IV)

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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.• We present TransLPC, a novel transformer-based object detector that is applicable to large point clouds. • A novel query refinement method is presented, which

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Section: Query Refinementmentioning

confidence: 99%