Object DGCNN: 3D Object Detection using Dynamic Graphs

• URL: http://arxiv.org/abs/2110.06923v1
• Date: Wed, 13 Oct 2021 17:59:38 GMT
• Title: Object DGCNN: 3D Object Detection using Dynamic Graphs
• Authors: Yue Wang and Justin Solomon
• Abstract summary: 3D object detection often involves complicated training and testing pipelines. Inspired by recent non-maximum suppression-free 2D object detection models, we propose a 3D object detection architecture on point clouds.
• Score: 32.090268859180334
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 3D object detection often involves complicated training and testing pipelines, which require substantial domain knowledge about individual datasets. Inspired by recent non-maximum suppression-free 2D object detection models, we propose a 3D object detection architecture on point clouds. Our method models 3D object detection as message passing on a dynamic graph, generalizing the DGCNN framework to predict a set of objects. In our construction, we remove the necessity of post-processing via object confidence aggregation or non-maximum suppression. To facilitate object detection from sparse point clouds, we also propose a set-to-set distillation approach customized to 3D detection. This approach aligns the outputs of the teacher model and the student model in a permutation-invariant fashion, significantly simplifying knowledge distillation for the 3D detection task. Our method achieves state-of-the-art performance on autonomous driving benchmarks. We also provide abundant analysis of the detection model and distillation framework.

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