TriSAR-3D and PaintFusion: A Novel Trimodal LiDAR-RGB-Thermal Dataset and Method for 3D Person Detection in Search and Rescue
To investigate this gap, we introduce TriSAR-3D, a multimodal person detection dataset recorded with a high-resolution 128-channel LiDAR, an RGB camera, and a thermal camera.
ProofPaper ↗
Key points
- Reliable 3D person detection is important for rescue robotics to give first responders situational awareness.
- Yet most multimodal 3D detection research targets automotive scenarios in which pedestrians mostly appear in a narrow range of upright, near-road poses.
- Furthermore, we propose a novel trimodal 3D detection pipeline, PaintFusion, that fuses PointPainting-based evidence using a simple MLP.
- We show that the derived method reaches 65.4 AP@50 and thereby outperforms strong LiDAR-only (CenterPoint, 53.4), RGB+LiDAR (BEVFusion, 60.5), and adapted trimodal (Tri-BEVFusion, 60.7) baselines on the dataset.
Sources (1)
- [1]TriSAR-3D and PaintFusion: A Novel Trimodal LiDAR-RGB-Thermal Dataset and Method for 3D Person Detection in Search and RescuearXiv (AI, ML, NLP, CV, robotics, multi-agent) · Oct 15, 06:03 PM
To investigate this gap, we introduce TriSAR-3D, a multimodal person detection dataset recorded with a high-resolution 128-channel LiDAR, an RGB camera, and a thermal camera.
Reliable 3D person detection is important for rescue robotics to give first responders situational awareness.
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