Research map / 3D vision / Face and shape
Implicit Functions in Feature Space for 3D Shape Reconstruction and Completion
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Measured effects reported in the paper
| What was changed | Where | Measured | Effect |
|---|---|---|---|
| Use multi-scale, spatially aligned 3D feature grids and query their features instead of feeding point coordinates and a single latent vector | 3D reconstruction and completion · ShapeNet point cloud completion | IoU: 0.73 -> 0.79; 0.72 -> 0.88 | Helped |
| Use multi-scale, spatially aligned 3D feature grids and query their features instead of feeding point coordinates and a single latent vector | 3D reconstruction and completion · ShapeNet voxel super-resolution | IoU: 0.60 -> 0.73; 0.71 -> 0.92 | Helped |
| Use multi-scale, spatially aligned 3D feature grids and query their features instead of feeding point coordinates and a single latent vector | 3D reconstruction and completion · Humans voxel super-resolution | IoU: 0.77 -> 0.80; 0.85 -> 0.96 | Helped |
| Use IF-Nets for single-view partial point-cloud completion | IF-Nets · Humans single-view reconstruction | IoU: 0.86 | Helped |
Closest papers by meaning
- Local Deep Implicit Functions for 3D Shape (CVPR 2020 · similarity 0.91)
- Neural Feature Matching in Implicit 3D Representations (ICML 2021 · similarity 0.89)
- Shape Completion Using 3D-Encoder-Predictor CNNs and Shape Synthesis (CVPR 2017 · similarity 0.89)
- Deformable Shape Completion With Graph Convolutional Autoencoders (CVPR 2018 · similarity 0.89)
- LP-DIF: Learning Local Pattern-Specific Deep Implicit Function for 3D Objects and Scenes (CVPR 2023 · similarity 0.89)
- Learning Partonomic 3D Reconstruction from Image Collections (CVPR 2025 · similarity 0.88)
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