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.

本文介绍了一种名为 OcTr 的基于八叉树的 Transformer 方法，通过动态构建八叉树从而实现对远距离或 / 和遮挡物体的有效检测，进一步提出了一种混合位置编码方法以增强前景感知，实验表明 OcTr 方法在 Waymo 开放数据集和 KITTI 数据集上均取得了最新的最佳结果。