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Research map / 3D vision / Depth and stereo

Depth Estimation by Combining Binocular Stereo and Monocular Structured-Light

CVPR 2022 · 3D vision › Depth and stereo · approach: Learned stereo with specialized sensors · 14 citations (OpenAlex)

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Measured effects reported in the paper

What was changedWhereMeasuredEffect
Add MSL disparity guidance to RAFT trained on mixed data (ablation)RAFT-OM on MonoBinoStereo · MonoBinoStereo, projector onBad2.0 (%): 14.60 -> 3.59Helped
Add MSL disparity guidance to PSMNet trained on mixed data (ablation)PSMNet-OM on MonoBinoStereo · MonoBinoStereo, projector onBad2.0 (%): 24.81 -> 3.81Helped
Add modified-data training to original-data training (ablation)RAFT on MonoBinoStereo · MonoBinoStereo, projector onBad2.0 (%): 21.88 -> 14.60Helped
Add modified-data training to original-data training (ablation)PSMNet on MonoBinoStereo · MonoBinoStereo, projector onBad2.0 (%): 41.79 -> 24.81Helped
Train on modified data only rather than original data (ablation)PSMNet · SceneFlow modified test setEPE: 3.922 -> 0.955Helped
Train on modified data only rather than original data (ablation)PSMNet · SceneFlow original test setEPE: 0.895 -> 1.212Hurt
Train on modified data only rather than original data (ablation)RAFT · SceneFlow modified test setEPE: 1.910 -> 1.092Helped
Train on modified data only rather than original data (ablation)RAFT · SceneFlow original test setEPE: 0.985 -> 1.070Hurt
Use mixed original and modified training data instead of original data alone (ablation)RAFT · MonoBinoStereo, projector offBad2.0 (%): 12.71 -> 11.72Helped

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