Spaces:
Running
Running
File size: 1,465 Bytes
84195c2 b7c4f7f 84195c2 b7c4f7f 84195c2 b7c4f7f b3bfd8a b7c4f7f b3bfd8a b7c4f7f b3bfd8a b7c4f7f b3bfd8a b7c4f7f b3bfd8a b7c4f7f b3bfd8a b7c4f7f |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 |
---
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**.
---
## 🚀 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.
---
## 📦 Outputs
- **Depth map** (colorized preview)
- **Point cloud (.ply)**
- **Mesh (.obj)** (with Gradio 3D viewer)
- **Mesh preview PNG** (best-effort offscreen render, if available)
---
## ⚠️ 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.
---
## 🛠️ Local Development
```bash
git clone <this-space>
cd <this-space>
pip install -r requirements.txt
python app.py
|