InfoFocus: 3D Object Detection for Autonomous Driving with Dynamic Information Modeling

ECCV 2020  ·  Jun Wang, Shiyi Lan, Mingfei Gao, Larry S. Davis ·

Real-time 3D object detection is crucial for autonomous cars. Achieving promising performance with high efficiency, voxel-based approaches have received considerable attention. However, previous methods model the input space with features extracted from equally divided sub-regions without considering that point cloud is generally non-uniformly distributed over the space. To address this issue, we propose a novel 3D object detection framework with dynamic information modeling. The proposed framework is designed in a coarse-to-fine manner. Coarse predictions are generated in the first stage via a voxel-based region proposal network. We introduce InfoFocus, which improves the coarse detections by adaptively refining features guided by the information of point cloud density. Experiments are conducted on the large-scale nuScenes 3D detection benchmark. Results show that our framework achieves the state-of-the-art performance with 31 FPS and improves our baseline significantly by 9.0% mAP on the nuScenes test set.

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Results from the Paper

Task Dataset Model Metric Name Metric Value Global Rank Result Benchmark
3D Object Detection nuScenes InfoFocus NDS 0.4 # 328
mAP 0.39 # 289
mATE 0.36 # 198
mASE 0.26 # 47
mAOE 1.13 # 13
mAVE 1.0 # 58
mAAE 0.4 # 29


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