Research map / 3D vision / Face and shape
GIFT: A Real-Time and Scalable 3D Shape Search Engine
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Measured effects reported in the paper
| What was changed | Where | Measured | Effect |
|---|---|---|---|
| Use GIFT instead of the strongest listed baseline | 3D shape retrieval · ModelNet40 | AUC: 77.63% -> 83.10% | Helped |
| Use GIFT instead of the strongest listed baseline | 3D shape retrieval · ModelNet10 | MAP: 84.18% -> 91.12% | Helped |
| Use GIFT instead of LCDR-DBSVC | 3D shape retrieval · SHREC14LSGTB | FT: 0.528 -> 0.567 | Helped |
| Use GIFT instead of TLC | 3D shape retrieval · WM-SHREC07 | FT: 0.831 -> 0.949 | Helped |
| Fuse L5 and L7 features with re-ranking (ablation) | L5 and L7 retrieval on PSB · PSB | First Tier: 0.653 -> 0.688 | Helped |
| Set aggregation exponent alpha to 0.5 rather than 1 (ablation) | L5 + L7 re-ranking on PSB · PSB | First Tier: 0.688 -> 0.692 | Helped |
| Add neighbor augmentation (ablation) | L5 + L7 re-ranking on PSB · PSB | First Tier: 0.692 -> 0.717 | Helped |
| Replace exact Hausdorff matching with approximate F-IF matching (ablation) | L5 + L7 with re-ranking on PSB · PSB | First Tier: 0.717 -> 0.712 | Hurt |
| Use approximate Hausdorff matching with F-IF rather than exact matching | L7 matching on SHREC14LSGTB · SHREC14LSGTB | query time: $4.82s$ -> $63.14ms$ | Helped |
Closest papers by meaning
- Duoduo CLIP: Efficient 3D Understanding with Multi-View Images (ICLR 2025 · similarity 0.83)
- KP-RED: Exploiting Semantic Keypoints for Joint 3D Shape Retrieval and Deformation (CVPR 2024 · similarity 0.83)
- Tango3D: Towards Alignment for Global and Local 2D-3D Correspondence (NeurIPS 2026 · similarity 0.83)
- PyTorchGeoNodes: Enabling Differentiable Shape Programs for 3D Shape Reconstruction (CVPR 2025 · similarity 0.83)
- MetaFind: Scene-Aware 3D Asset Retrieval for Coherent Metaverse Scene Generation (NeurIPS 2025 · similarity 0.83)
- SpiderMatch: 3D Shape Matching with Global Optimality and Geometric Consistency (CVPR 2024 · similarity 0.82)
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