A radar-camera depth estimation framework that recalibrates sparse radar points and aligns a frozen monocular depth model using sparse LiDAR labels, claiming state-of-the-art accuracy with roughly 1% supervision density.
Compensation for positional errors in mmw radar slam caused by observation deviations and coordinate system trans- formation,
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RaCalNet: Radar Calibration Network for Sparse-Supervised Metric Depth Estimation
A radar-camera depth estimation framework that recalibrates sparse radar points and aligns a frozen monocular depth model using sparse LiDAR labels, claiming state-of-the-art accuracy with roughly 1% supervision density.