Research map / 3D vision / Depth and stereo
Learning Dense Correspondence via 3D-Guided Cycle Consistency
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Measured effects reported in the paper
| What was changed | Where | Measured | Effect |
|---|---|---|---|
| Fine-tune with 3D-guided cycle-consistency objectives instead of using SIFT flow initialization alone (ablation) | CNN_init · PASCAL3D+ val | PCK (α = 0.1), 12-category mean: 17.7 -> 24.0 | Helped |
| Fine-tune with 3D-guided cycle consistency rather than synthetic ground-truth correspondence (ablation) | CNN_init · PASCAL3D+ val | PCK (α = 0.1), 12-category mean: 18.4 -> 24.0 | Helped |
| Fine-tune on synthetic image pairs with ground-truth correspondence (ablation) | CNN_init · PASCAL3D+ val | PCK (α = 0.1), 12-category mean: 17.7 -> 18.4 | Helped |
| Use 3D-guided cycle-consistency training instead of SIFT flow | Dense correspondence · PASCAL3D+ val | PCK (α = 0.1), 12-category mean: 19.6 -> 24.0 | Helped |
| Use 3D-guided cycle-consistency training instead of SIFT flow | Matchability prediction · PASCAL3D+ test image pairs | matchability classification accuracy, 12-category mean: 57.1 -> 67.8 | Helped |
| Use cycle-consistency training rather than SIFT flow | Dense correspondence · PASCAL3D+ val, bicycle and motorbike categories | PCK (α = 0.1): bicycle: 23.3 vs. 22.3; motorbike: 17.9 vs. 17.2 | Mixed |
Closest papers by meaning
- Self-Supervised Cross-View Correspondence with Predictive Cycle Consistency (CVPR 2025 · similarity 0.90)
- Learning Dense Correspondences between Photos and Sketches (ICML 2023 · similarity 0.88)
- Universal Correspondence Network (NeurIPS 2016 · similarity 0.88)
- SFNet: Learning Object-Aware Semantic Correspondence (CVPR 2019 · similarity 0.88)
- Jamais Vu: Exposing the Generalization Gap in Supervised Semantic Correspondence (NeurIPS 2025 · similarity 0.86)
- SemAlign3D: Semantic Correspondence between RGB-Images through Aligning 3D Object-Class Representations (CVPR 2025 · similarity 0.86)
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