README.md

metadata

title: 2D → 3D Reconstruction (GLPN + Open3D)
emoji: 🏠
colorFrom: indigo
colorTo: purple
sdk: gradio
sdk_version: 4.29.0
app_file: app.py
pinned: false
license: mit
tags:
  - depth-estimation
  - monocular
  - 3d-reconstruction
  - open3d
  - point-cloud
  - mesh
  - gradio
  - huggingface

2D → 3D Reconstruction (GLPN + Open3D)

This Space estimates monocular depth from a single RGB image using GLPN, builds an RGB-D point cloud, and reconstructs a 3D mesh with Poisson surface reconstruction via Open3D.

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🚀 How it works

1. Upload an image.
2. GLPN (NYU pretrained) → predict relative depth.
3. Open3D → convert RGB + depth → point cloud.
4. Poisson reconstruction → mesh (downloadable .obj and .ply).
5. Preview depth map, mesh snapshot, and explore the mesh interactively.

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📦 Outputs

• Depth map (colorized preview)
• Point cloud (.ply)
• Mesh (.obj) (with Gradio 3D viewer)
• Mesh preview PNG (best-effort offscreen render, if available)

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⚠️ Notes

• Monocular depth has no absolute scale → geometry is up-to-scale only.
• For metric accuracy, swap in stereo, multi-view SfM, or metric depth models (ZoeDepth, Depth Anything v2).
• Works on CPU or GPU Spaces. GPU recommended for faster inference.

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🛠️ Local Development

git clone <this-space>
cd <this-space>
pip install -r requirements.txt
python app.py