Patric Jensfelt

Radar data, often relegated to a learned feature, can unlock significant gains in 3D multi-object tracking robustness, especially when other sensors falter in adverse conditions or at long ranges.

Forget unreliable two-frame supervision: TeFlow unlocks 33% better scene flow estimation by mining temporally consistent motion cues across multiple frames for self-supervised feed-forward models.