OcTr: Octree-based Transformer for 3D Object Detection
This work addresses a key problem in autonomous driving by improving 3D object detection for distant or occluded objects, representing an incremental advancement over existing transformer methods.
The paper tackles the challenge of capturing sufficient features for distant or occluded objects in LiDAR-based 3D object detection by proposing OcTr, an octree-based transformer that balances accuracy and efficiency, achieving state-of-the-art results on Waymo Open and KITTI datasets.
A key challenge for LiDAR-based 3D object detection is to capture sufficient features from large scale 3D scenes especially for distant or/and occluded objects. Albeit recent efforts made by Transformers with the long sequence modeling capability, they fail to properly balance the accuracy and efficiency, suffering from inadequate receptive fields or coarse-grained holistic correlations. In this paper, we propose an Octree-based Transformer, named OcTr, to address this issue. It first constructs a dynamic octree on the hierarchical feature pyramid through conducting self-attention on the top level and then recursively propagates to the level below restricted by the octants, which captures rich global context in a coarse-to-fine manner while maintaining the computational complexity under control. Furthermore, for enhanced foreground perception, we propose a hybrid positional embedding, composed of the semantic-aware positional embedding and attention mask, to fully exploit semantic and geometry clues. Extensive experiments are conducted on the Waymo Open Dataset and KITTI Dataset, and OcTr reaches newly state-of-the-art results.