3D Object Detection with Pointformer

• URL: http://arxiv.org/abs/2012.11409v1
• Date: Mon, 21 Dec 2020 15:12:54 GMT
• Title: 3D Object Detection with Pointformer
• Authors: Xuran Pan, Zhuofan Xia, Shiji Song, Li Erran Li, Gao Huang
• Abstract summary: We propose Pointformer, a Transformer backbone designed for 3D point clouds to learn features effectively. 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.
• Score: 29.935891419574602
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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.

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