HongshengY/S2V_Net

S2V-Net for NeuralSketch2Surf

This repository hosts the pretrained S2V-Net weights used by NeuralSketch2Surf: Fast Neural Surfacing of Unoriented 3D Sketches.

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.

Model

• 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.

Notes

• The model is trained for closed-surface reconstruction.
• Very thin structures may be limited by the 112^3 voxel resolution.

Citation

If you use these weights, please cite:

@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}
}