Research map / 3D vision / Depth and stereo
Unsupervised Learning of Depth and Ego-Motion From Monocular Video Using 3D Geometric Constraints
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Measured effects reported in the paper
| What was changed | Where | Measured | Effect |
|---|---|---|---|
| add the 3D ICP alignment loss (ablation) | Ours · KITTI Eigen test, K training | Abs Rel: 0.175 -> 0.163 | Helped |
| add the 3D ICP alignment loss (ablation) | Ours · KITTI Eigen test, Bike training | Abs Rel: 0.226 -> 0.211 | Helped |
| add the SSIM loss (ablation) | Ours · KITTI Eigen test, K training | Abs Rel: 0.183 -> 0.163 | Helped |
| add principled validity masks (ablation) | Ours · KITTI Eigen test, K training | Abs Rel: 0.176 -> 0.163 | Helped |
| train on Cityscapes in addition to KITTI | Ours · KITTI Eigen test, 80m cap | Abs Rel: 0.163 -> 0.159 | Helped |
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- Scalable Benchmarking and Robust Learning for Noise-Free Ego-Motion and 3D Reconstruction from Noisy Video (ICLR 2025 · similarity 0.87)
- 3D Human Pose Perception from Egocentric Stereo Videos (CVPR 2024 · similarity 0.87)
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