Research map / Point clouds / Shape and surface completion
DeepCurrents: Learning Implicit Representations of Shapes With Boundaries
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Measured effects reported in the paper
| What was changed | Where | Measured | Effect |
|---|---|---|---|
| Use DeepCurrents instead of the baseline reconstruction method | Single-surface reconstruction · FAUST head | UCD: 0.0049 -> 0.0010 | Helped |
| Use DeepCurrents instead of the baseline reconstruction method | Single-surface reconstruction · FAUST hand | UCD: 0.0045 -> 0.0011 | Helped |
| Use DeepCurrents instead of the baseline reconstruction method | Single-surface reconstruction · FAUST torso | UCD: 0.0049 -> 0.00092 | Helped |
| Use DeepCurrents instead of the baseline reconstruction method | Single-surface reconstruction · FAUST foot | UCD: 0.0055 -> 0.00092 | Helped |
| Add random Fourier feature input projection (ablation) | DeepCurrents surface reconstruction · FAUST hand | learnability | Helped |
| Add the surface loss term (ablation) | DeepCurrents surface reconstruction · FAUST hand | surface recovery | Helped |
| Add boundary weighting to the current loss (ablation) | DeepCurrents surface reconstruction · FAUST hand | boundary sharpness | Helped |
| Use softplus instead of ReLU activations (ablation) | DeepCurrents decoder · FAUST hand | surface sharpness | Helped |
Closest papers by meaning
- Local Deep Implicit Functions for 3D Shape (CVPR 2020 · similarity 0.89)
- Learning to Infer Implicit Surfaces without 3D Supervision (NeurIPS 2019 · similarity 0.89)
- Learning Manifold Patch-Based Representations of Man-Made Shapes (ICLR 2021 · similarity 0.88)
- Neural Convolutional Surfaces (CVPR 2022 · similarity 0.88)
- SurfNet: Generating 3D Shape Surfaces Using Deep Residual Networks (CVPR 2017 · similarity 0.88)
- Implicit Geometric Regularization for Learning Shapes (ICML 2020 · similarity 0.88)
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