3D Object Detection with Pointformer


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-theart object detection models and demonstrate significant improvements over original models on both indoor and outdoor datasets. Code and pre-trained models are available at https://github.com/Vladimir2506/Pointformer.

In IEEE Conference on Computer Vision and Pattern Recognition (CVPR) 2021
Xuran Pan
Xuran Pan
Ph.D. Student

My research interests lie in model architecuture design, graph neural network and 3D computer vision.