CR3DT: Camera-RADAR Fusion for 3D Detection and Tracking

• URL: http://arxiv.org/abs/2403.15313v2
• Date: Tue, 6 Aug 2024 15:58:35 GMT
• Title: CR3DT: Camera-RADAR Fusion for 3D Detection and Tracking
• Authors: Nicolas Baumann, Michael Baumgartner, Edoardo Ghignone, Jonas Kühne, Tobias Fischer, Yung-Hsu Yang, Marc Pollefeys, Michele Magno,
• Abstract summary: Camera-RADAR 3D Detection and Tracking (CR3DT) is a camera-RADAR fusion model for 3D object detection, and Multi-Object Tracking (MOT) Building upon the foundations of the State-of-the-Art (SotA) camera-only BEVDet architecture, CR3DT demonstrates substantial improvements in both detection and tracking capabilities.
• Score: 40.630532348405595
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: To enable self-driving vehicles accurate detection and tracking of surrounding objects is essential. While Light Detection and Ranging (LiDAR) sensors have set the benchmark for high-performance systems, the appeal of camera-only solutions lies in their cost-effectiveness. Notably, despite the prevalent use of Radio Detection and Ranging (RADAR) sensors in automotive systems, their potential in 3D detection and tracking has been largely disregarded due to data sparsity and measurement noise. As a recent development, the combination of RADARs and cameras is emerging as a promising solution. This paper presents Camera-RADAR 3D Detection and Tracking (CR3DT), a camera-RADAR fusion model for 3D object detection, and Multi-Object Tracking (MOT). Building upon the foundations of the State-of-the-Art (SotA) camera-only BEVDet architecture, CR3DT demonstrates substantial improvements in both detection and tracking capabilities, by incorporating the spatial and velocity information of the RADAR sensor. Experimental results demonstrate an absolute improvement in detection performance of 5.3% in mean Average Precision (mAP) and a 14.9% increase in Average Multi-Object Tracking Accuracy (AMOTA) on the nuScenes dataset when leveraging both modalities. CR3DT bridges the gap between high-performance and cost-effective perception systems in autonomous driving, by capitalizing on the ubiquitous presence of RADAR in automotive applications. The code is available at: https://github.com/ETH-PBL/CR3DT.

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