Research map / 3D vision / Face and shape
Isometric Non-Rigid Shape-From-Motion in Linear Time
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Measured effects reported in the paper
| What was changed | Where | Measured | Effect |
|---|---|---|---|
| Use the proposed Riemannian, point-wise Iso-NRSfM method instead of Gotardo | Iso-NRSfM · Rug dataset | depth error (mm): 67.1 vs 34.9 | Helped |
| Use the proposed Riemannian, point-wise Iso-NRSfM method instead of Gotardo | Iso-NRSfM · Rug dataset | shape error (°): 19.8 vs 16.7 | Helped |
| Use the proposed Riemannian, point-wise Iso-NRSfM method instead of Gotardo | Iso-NRSfM · Cat dataset | depth error (mm): 17.8 vs 9.6 | Helped |
| Use the proposed Riemannian, point-wise Iso-NRSfM method instead of Gotardo | Iso-NRSfM · Cat dataset | shape error (°): 19.2 vs 16.9 | Helped |
| Use the proposed Riemannian, point-wise Iso-NRSfM method instead of Gotardo | Iso-NRSfM · Kinect paper dataset | depth error (mm): 20.6 vs 7.1 | Helped |
| Use the proposed Riemannian, point-wise Iso-NRSfM method instead of Gotardo | Iso-NRSfM · Kinect paper dataset | shape error (°): 18.7 vs 9.6 | Helped |
| Increase missing data from 0% to 50% | Our method · Rug dataset | depth error (mm): 28.8 -> 30.7 | Hurt |
| Increase missing data from 0% to 50% | Our method · Cat dataset | depth error (mm): 4.8 -> 7.9 | Hurt |
| Increase missing data from 0% to 50% | Our method · Kinect paper dataset | depth error (mm): 7.1 -> 9.7 | Hurt |
Closest papers by meaning
- Non-Rigid Structure-from-Motion: Temporally-Smooth Procrustean Alignment and Spatially-Variant Deformation Modeling (CVPR 2024 · similarity 0.86)
- Hybrid Functional Maps for Crease-Aware Non-Isometric Shape Matching (CVPR 2024 · similarity 0.85)
- Deep Non-Rigid Structure-from-Motion Revisited: Canonicalization and Sequence Modeling (AAAI 2025 · similarity 0.85)
- 4Deform: Neural Surface Deformation for Robust Shape Interpolation (CVPR 2025 · similarity 0.84)
- Neural Parametric Gaussians for Monocular Non-Rigid Object Reconstruction (CVPR 2024 · similarity 0.84)
- Neural Isometries: Taming Transformations for Equivariant ML (NeurIPS 2024 · similarity 0.84)
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