| --- |
| license: mit |
| language: |
| - en |
| tags: |
| - 3d |
| - 3d-reconstruction |
| - sketch-based-modeling |
| - surface-reconstruction |
| - pytorch |
| - siggraph-2026 |
| --- |
| |
| # S2V-Net for NeuralSketch2Surf |
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| This repository hosts the pretrained S2V-Net weights used by **NeuralSketch2Surf: Fast Neural Surfacing of Unoriented 3D Sketches**. |
|
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| S2V-Net reconstructs closed surfaces from sparse, unoriented 3D sketch curves. The model predicts a `112^3` volumetric occupancy field from voxelized sketch strokes; the final mesh is extracted with Marching Cubes and can be refined with the smoothing tool from the project repository. |
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| ## Model |
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| - **Input:** voxelized 3D sketch strokes on a `112^3` grid. |
| - **Output:** binary surface occupancy probabilities. |
| - **Backbone:** SwinUNETR-style 3D transformer for global shape inference. |
| - **Refinement:** lightweight 3D residual module for local boundary correction. |
| - **Use case:** interactive sketch-based surface reconstruction from raw 3D strokes. |
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| ## Notes |
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| - The model is trained for closed-surface reconstruction. |
| - Very thin structures may be limited by the `112^3` voxel resolution. |
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|
| ## Citation |
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| If you use these weights, please cite: |
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| ```bibtex |
| @article{ye2026neuralsketch2surf, |
| title = {NeuralSketch2Surf: Fast Neural Surfacing of Unoriented 3D Sketches}, |
| author = {Ye, Hongsheng and Sureshkumar, Anandhu and Wang, Zhonghan and Cani, Marie-Paule and Hahmann, Stefanie and Bonneau, Georges-Pierre and Parakkat, Amal Dev}, |
| journal = {ACM Transactions on Graphics (Proceedings of SIGGRAPH)}, |
| year = {2026} |
| } |
| ``` |
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|
