Update README.md

README.md

@@ -1,3 +1,44 @@
----
-license: mit
----
+---
+license: mit
+language:
+- en
+tags:
+- 3D
+- 3D-Reconstruction
+- Sketch-to-3D
+- Transformer
+- Pytorch
+---
+
+# S2V-Net (NeuralSketch2Surf)
+
+S2V-Net is the core model from the paper **"NeuralSketch2Surf: Fast Neural Surfacing of Unoriented 3D Sketches"**. It instantly converts sparse, unoriented 3D sketches (like those drawn in VR) into smooth, closed 3D meshes.
+
+## Core Highlights
+
+*   **Lightning Fast (Interactive Rates):** The entire pipeline takes **< 0.7 seconds** on a GPU, making it perfectly suited for real-time VR applications.
+*   **No Normals Required:** Unlike many existing methods, it processes completely unoriented, raw 3D strokes. Users don't need to worry about stroke directions.
+*   **Robust & Accurate:** Accurately fills large spatial gaps between sparse strokes while preserving high-frequency geometric details. 
+
+## How it Works
+
+The pipeline treats 3D surfacing as a binary voxel-occupancy prediction task operating on a $112^3$ grid:
+1. **Backbone (Global Shape):** A custom **SwinUNETR v2** transformer infers the global topology and bridges large gaps between sparse input strokes.
+2. **Refinement (Local Details):** A lightweight 3D CNN acts as a geometric denoiser to sharpen boundaries and recover fine details.
+3. **Meshing:** The predicted occupancy grid is extracted via Marching Cubes and smoothed using a locally controllable Laplacian filter.
+
+## Limitations
+*   **Closed Surfaces Only:** The model assumes the input sketch represents a solid, closed object. It is not designed for open surfaces.
+
+## Citation
+
+If you use S2V-Net in your research or project, please cite:
+
+```bibtex
+@inproceedings{neuralsketch2surf2026,
+  author = {Anonymous Author(s)},
+  title = {NeuralSketch2Surf: Fast Neural Surfacing of Unoriented 3D Sketches},
+  booktitle = {Proceedings of ACM Trans. Graph.},
+  year = {2026},
+  publisher = {ACM}
+}