Research map / Point clouds / Shape and surface completion
Learning Deep Implicit Functions for 3D Shapes With Dynamic Code Clouds
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Measured effects reported in the paper
| What was changed | Where | Measured | Effect |
|---|---|---|---|
| Replace grid-constrained code locations with continuous learnable position vectors (ablation) | DCC-DIF · ShapeNet display | Chamfer: 0.045 -> 0.033 | Helped |
| Replace grid-constrained code locations with continuous learnable position vectors (ablation) | DCC-DIF · ShapeNet watercraft | Chamfer: 0.108 -> 0.081 | Helped |
| Add CP loss to the model with position vectors (ablation) | DCC-DIF · ShapeNet display | Chamfer: 0.033 -> 0.028 | Helped |
| Add CP loss to the model with position vectors (ablation) | DCC-DIF · ShapeNet watercraft | Chamfer: 0.081 -> 0.042 | Helped |
| Use trilinear interpolation with codes fixed at grid points instead of the proposed distance-based interpolation (ablation) | DCC-DIF · ShapeNet display | Chamfer: 0.045 -> 0.038 | Helped |
| Use trilinear interpolation with codes fixed at grid points instead of the proposed distance-based interpolation (ablation) | DCC-DIF · ShapeNet watercraft | Chamfer: 0.108 -> 0.090 | Helped |
Closest papers by meaning
- LP-DIF: Learning Local Pattern-Specific Deep Implicit Function for 3D Objects and Scenes (CVPR 2023 · similarity 0.93)
- Local Deep Implicit Functions for 3D Shape (CVPR 2020 · similarity 0.90)
- DeepSDF: Learning Continuous Signed Distance Functions for Shape Representation (CVPR 2019 · similarity 0.89)
- A Lightweight UDF Learning Framework for 3D Reconstruction Based on Local Shape Functions (CVPR 2025 · similarity 0.88)
- LISR: Learning Linear 3D Implicit Surface Representation Using Compactly Supported Radial Basis Functions (AAAI 2024 · similarity 0.86)
- Few-Shot Unsupervised Implicit Neural Shape Representation Learning with Spatial Adversaries (ICML 2024 · similarity 0.86)
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