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Hidden Gems: 4D Radar Scene Flow Learning Using Cross-Modal Supervision

CVPR 2023 · Vision-language and multimodal › Cross-modal learning · approach: Unimodal-to-multimodal representation transfer · 46 citations (OpenAlex)

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Measured effects reported in the paper

What was changedWhereMeasuredEffect
Use cross-modal supervision from odometer, LiDAR, and camera instead of no modality supervision (ablation)CMFlow · VoD TestEPE [m]: 0.228 -> 0.141Helped
Add odometer supervision (ablation)CMFlow · VoD TestEPE [m]: 0.228 -> 0.161Helped
Add LiDAR supervision to odometer supervision (ablation)CMFlow · VoD TestEPE [m]: 0.161 -> 0.145Helped
Add camera supervision to odometer supervision (ablation)CMFlow · VoD TestEPE [m]: 0.161 -> 0.159Helped
Add temporal update module (ablation)CMFlow · VoD TestEPE [m]: 0.141 -> 0.130Helped
Add temporal update module (ablation)CMFlow · VoD TestAccS: 0.233 -> 0.228Hurt
Use odometer-derived moving label S^v (ablation)CMFlow motion segmentation · VoD TestmIoU (%): 46.9 -> 52.8Helped
Add LiDAR-derived label S^l to S^v (ablation)CMFlow motion segmentation · VoD TestmIoU (%): 52.8 -> 54.1Helped
Add extra unannotated training data (ablation)CMFlow motion segmentation · VoD TestmIoU (%): 54.1 -> 57.1Helped
Add odometer supervision (ablation)CMFlow ego-motion estimation · VoD TestRAE [°]: 0.336 -> 0.183Helped
Add LiDAR and camera supervision to odometer supervision (ablation)CMFlow ego-motion estimation · VoD TestRAE [°]: 0.183 -> 0.145Helped
Add temporal update module (ablation)CMFlow ego-motion estimation · VoD TestRTE [m]: 0.071 -> 0.066Helped

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