OcTr: Octree-based Transformer for 3D Object Detection
- URL: http://arxiv.org/abs/2303.12621v1
- Date: Wed, 22 Mar 2023 15:01:20 GMT
- Title: OcTr: Octree-based Transformer for 3D Object Detection
- Authors: Chao Zhou, Yanan Zhang, Jiaxin Chen, Di Huang
- Abstract summary: A key challenge for LiDAR-based 3D object detection is to capture sufficient features from large scale 3D scenes.
We propose an Octree-based Transformer, named OcTr, to address this issue.
For enhanced foreground perception, we propose a hybrid positional embedding, composed of the semantic-aware positional embedding and attention mask.
- Score: 30.335788698814444
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: 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.
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