  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.
