Introduces a 9136-sample multi-view in-cabin dataset from a German city bus with RGB, depth, LiDAR, 3D annotations via pseudo-labeling, nuScenes conversion, and benchmarks on models like BEVFusion.
arXiv preprint arXiv:2302.05094 (2023)
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A framework that keeps 3D Gaussian Splatting geometry metric-accurate for LiDAR-camera calibration by using multi-view LiDAR depth supervision and blocking photometric updates to spatial parameters, outperforming prior targetless methods on driving datasets.
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Multi-View In-Cabin Monitoring System for Public Transport Vehicles
Introduces a 9136-sample multi-view in-cabin dataset from a German city bus with RGB, depth, LiDAR, 3D annotations via pseudo-labeling, nuScenes conversion, and benchmarks on models like BEVFusion.
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Geometry-Preserving in 3D Gaussian Splatting for LiDAR-Camera Extrinsic Calibration
A framework that keeps 3D Gaussian Splatting geometry metric-accurate for LiDAR-camera calibration by using multi-view LiDAR depth supervision and blocking photometric updates to spatial parameters, outperforming prior targetless methods on driving datasets.