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

• URL: http://arxiv.org/abs/2007.08556v1
• Date: Thu, 16 Jul 2020 18:27:08 GMT
• Title: InfoFocus: 3D Object Detection for Autonomous Driving with Dynamic Information Modeling
• Authors: Jun Wang, Shiyi Lan, Mingfei Gao, Larry S. Davis
• Abstract summary: We propose a novel 3D object detection framework with dynamic information modeling. Coarse predictions are generated in the first stage via a voxel-based region proposal network. Experiments are conducted on the large-scale nuScenes 3D detection benchmark.
• Score: 65.47126868838836
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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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