  Effectively preserving and encoding structure features from objects in
irregular and sparse LiDAR points is a key challenge to 3D object detection on
point cloud. Recently, Transformer has demonstrated promising performance on
many 2D and even 3D vision tasks. Compared with the fixed and rigid convolution
kernels, the self-attention mechanism in Transformer can adaptively exclude the
unrelated or noisy points and thus suitable for preserving the local spatial
structure in irregular LiDAR point cloud. However, Transformer only performs a
simple sum on the point features, based on the self-attention mechanism, and
all the points share the same transformation for value. Such isotropic
operation lacks the ability to capture the direction-distance-oriented local
structure which is important for 3D object detection. In this work, we propose
a Structure-Embedding transFormer (SEFormer), which can not only preserve local
structure as traditional Transformer but also have the ability to encode the
local structure. Compared to the self-attention mechanism in traditional
Transformer, SEFormer learns different feature transformations for value points
based on the relative directions and distances to the query point. Then we
propose a SEFormer based network for high-performance 3D object detection.
Extensive experiments show that the proposed architecture can achieve SOTA
results on Waymo Open Dataset, the largest 3D detection benchmark for
autonomous driving. Specifically, SEFormer achieves 79.02% mAP, which is 1.2%
higher than existing works. We will release the codes.
