Research map / 3D vision / Depth and stereo
Unsupervised Cross-Spectral Stereo Matching by Learning to Synthesize
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Measured effects reported in the paper
| What was changed | Where | Measured | Effect |
|---|---|---|---|
| Add spectral translation to the stereo matcher (ablation) | Only SMN · PittsStereo-RGBNIR Mean | disparity RMSE: 1.27 -> 1.18 | Helped |
| Replace original CycleGAN with F-cycleGAN for spectral translation (ablation) | STN + SMN · PittsStereo-RGBNIR Mean | disparity RMSE: 1.18 -> 1.04 | Helped |
| Add the auxiliary loss for spectral translation (ablation) | STN(F) + SMN · PittsStereo-RGBNIR Mean | disparity RMSE: 1.04 -> 0.84 | Helped |
| Use concatenated original and translated image pairs rather than original image pairs as SMN inputs (ablation) | STN(F) + SMN(aux)(ori) · PittsStereo-RGBNIR Mean | disparity RMSE: 0.87 -> 0.84 | Helped |
| Use F-cycleGAN with joint training and auxiliary loss | Cross-spectral stereo matching · PittsStereo-RGBNIR | disparity RMSE | Mixed |
Closest papers by meaning
- Deep Material-Aware Cross-Spectral Stereo Matching (CVPR 2018 · similarity 0.90)
- Adaptive Multi-Modal Cross-Entropy Loss for Stereo Matching (CVPR 2024 · similarity 0.85)
- Unsupervised Deep Asymmetric Stereo Matching With Spatially-Adaptive Self-Similarity (CVPR 2023 · similarity 0.85)
- Hierarchical Object-Aware Dual-Level Contrastive Learning for Domain Generalized Stereo Matching (NeurIPS 2024 · similarity 0.84)
- Cross-Spectral Gaussian Splatting with Spatial Occupancy Consistency (AAAI 2025 · similarity 0.83)
- Stereo Risk: A Continuous Modeling Approach to Stereo Matching (ICML 2024 · similarity 0.83)
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