  Feature learning for 3D object detection from point clouds is very
challenging due to the irregularity of 3D point cloud data. In this paper, we
propose Pointformer, a Transformer backbone designed for 3D point clouds to
learn features effectively. Specifically, a Local Transformer module is
employed to model interactions among points in a local region, which learns
context-dependent region features at an object level. A Global Transformer is
designed to learn context-aware representations at the scene level. To further
capture the dependencies among multi-scale representations, we propose
Local-Global Transformer to integrate local features with global features from
higher resolution. In addition, we introduce an efficient coordinate refinement
module to shift down-sampled points closer to object centroids, which improves
object proposal generation. We use Pointformer as the backbone for
state-of-the-art object detection models and demonstrate significant
improvements over original models on both indoor and outdoor datasets.
