Research map / Point clouds / Shape and surface completion
Similarity Metric For Curved Shapes In Euclidean Space
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Measured effects reported in the paper
| What was changed | Where | Measured | Effect |
|---|---|---|---|
| Use the proposed Lie-group curve representation and distance instead of the strongest reported baseline | Flavia leaf retrieval · Flavia leave-one-out | MAP: 92.37 -> 94.11 | Helped |
| Add Gaussian noise with standard deviation σ=0.5 | Proposed metric · Fighter jets dataset | MAP: 97.11 -> 96.72 | Hurt |
| Add Gaussian noise with standard deviation σ=1.5 | Proposed metric · Fighter jets dataset | MAP: 97.11 -> 89.95 | Hurt |
| Add Gaussian noise with standard deviation σ=2.5 | Proposed metric · Fighter jets dataset | MAP: 97.11 -> 83.27 | Hurt |
Closest papers by meaning
- Wormhole Loss for Partial Shape Matching (NeurIPS 2024 · similarity 0.85)
- Follow the Energy, Find the Path: Riemannian Metrics from Energy-Based Models (NeurIPS 2025 · similarity 0.84)
- Spectral Meets Spatial: Harmonising 3D Shape Matching and Interpolation (CVPR 2024 · similarity 0.84)
- HOPE: Shape Matching Via Aligning Different K-hop Neighbourhoods (NeurIPS 2024 · similarity 0.84)
- SpiderMatch: 3D Shape Matching with Global Optimality and Geometric Consistency (CVPR 2024 · similarity 0.83)
- Hybrid Functional Maps for Crease-Aware Non-Isometric Shape Matching (CVPR 2024 · similarity 0.83)
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