README.md

metadata

title: Depth Estimation Compare Demo
emoji: 👀
colorFrom: indigo
colorTo: indigo
sdk: gradio
sdk_version: 5.49.1
app_file: app.py
pinned: false

Depth Estimation Comparison Demo

A ZeroGPU-friendly Gradio interface for comparing Depth Anything v1, Depth Anything v2, and Pixel-Perfect Depth (PPD) on the same image. Switch between side-by-side layouts, a slider overlay, or single-model inspection to understand how different pipelines perceive scene geometry.

🚀 Highlights

• Three interactive views: draggable slider, labeled side-by-side comparison, and original vs depth for any single model.
• Multi-family depth models: run ViT variants from Depth Anything v1/v2 alongside Pixel-Perfect Depth with MoGe metric alignment.
• ZeroGPU aware: on-demand loading, model cache clearing, and torch CUDA cleanup keep GPU usage inside HuggingFace Spaces limits.
• Curated examples: reusable demo images sourced from each model family plus local assets to quickly validate behaviour.

🔍 Supported Pipelines

• Depth Anything v1 (LiheYoung/depth_anything_*): ViT-S/B/L with fast transformer backbones and colorized outputs via Spectral_r colormap.
• Depth Anything v2 (Depth-Anything-V2/checkpoints/*.pth): ViT-Small/Base/Large with HF Hub fallback, configurable feature channels, and improved edge handling.
• Pixel-Perfect Depth: Diffusion-based relative depth refined by the MoGe metric surface model and RANSAC alignment to recover metric depth; customizable denoising steps.

🖥️ App Experience

• Slider Comparison: drag between two predictions with automatically labeled overlays.
• Method Comparison: view models side-by-side with synchronized layout and captions rendered in OpenCV.
• Single Model: inspect the RGB input versus one model output using the Gradio ImageSlider component.
• Example Gallery: natural-number sorting across assets/examples, Depth-Anything/assets/examples, Depth-Anything-V2/assets/examples, and Pixel-Perfect-Depth/assets/examples.

📦 Installation & Setup

Local Development

1. Clone & enter:

   git clone <repository-url>
   cd Depth-Estimation-Compare-demo
   

2. Install dependencies (includes gradio, torch, gradio_imageslider, open3d, scikit-learn, and MoGe utilities):

   pip install -r requirements.txt
   

3. Model assets:
   • Depth Anything v1 checkpoints stream automatically from the HuggingFace Hub.
   • Download Depth Anything v2 weights into Depth-Anything-V2/checkpoints/ if they are not already present (depth_anything_v2_vits.pth, depth_anything_v2_vitb.pth, depth_anything_v2_vitl.pth).
   • Pixel-Perfect Depth pulls the diffusion checkpoint (ppd.pth) from gangweix/Pixel-Perfect-Depth on first use and loads MoGe weights (Ruicheng/moge-2-vitl-normal).
4. Run the app:

   python app_local.py   # Local UI with live reload tweaks
   python app.py         # ZeroGPU-ready launch script
   

HuggingFace Spaces (ZeroGPU)

1. Push the repository contents to a Gradio Space.
2. Select the ZeroGPU hardware preset.
3. The app will download required checkpoints on demand and aggressively free memory after each inference via clear_model_cache().

📁 Project Structure

Depth-Estimation-Compare-demo/
├── app.py                 # ZeroGPU deployment entrypoint
├── app_local.py           # Local-friendly launch script
├── requirements.txt       # Python dependencies (Gradio, Torch, PPD stack)
├── assets/
│   └── examples/          # Shared demo imagery
├── Depth-Anything/        # Depth Anything v1 implementation + utilities
├── Depth-Anything-V2/     # Depth Anything v2 implementation & checkpoints
├── Pixel-Perfect-Depth/   # Pixel-Perfect Depth diffusion + MoGe helpers
└── README.md              # You are here

⚙️ Configuration Notes

• Model dropdown labels come from V1_MODEL_CONFIGS, V2_MODEL_CONFIGS, and the PPD entry in app.py.
• clear_model_cache() resets every model and flushes CUDA to respect ZeroGPU constraints.
• Pixel-Perfect Depth inference aligns relative depth to metric scale through recover_metric_depth_ransac() for consistent visualization.
• Depth visualizations use a normalized Spectral_r colormap; PPD uses a dedicated matplotlib colormap for metric maps.

📊 Performance Expectations

• Depth Anything v1: ViT-S ~1–2 s, ViT-B ~2–4 s, ViT-L ~4–8 s (image dependent).
• Depth Anything v2: similar to v1 with improved sharpness; HF downloads add one-time startup overhead.
• Pixel-Perfect Depth: diffusion + metric refinement typically takes longer (10–20 denoise steps) but returns metrically-aligned depth suitable for downstream 3D tasks.

🎯 Usage Tips

• Mix-and-match any two models in comparison tabs to highlight qualitative differences.
• Use the Single Model tab to corroborate PPD metric depth versus RGB input.
• Leverage the provided examples to benchmark indoor/outdoor, lighting extremes, and complex geometry scenarios before running custom images.

🤝 Contributing

Enhancements are welcome—new model backends, visualization modes, or memory optimizations are especially valuable for ZeroGPU deployments. Please follow the coding style in app.py and keep documentation in sync with new capabilities.

📚 References

• Depth Anything v1
• Depth Anything v2
• Pixel-Perfect Depth
• MoGe

📄 License

• Depth Anything v1: MIT License
• Depth Anything v2: Apache 2.0 License
• Pixel-Perfect Depth: see upstream repository for licensing
• Demo scaffolding in this repo: MIT License (follow individual component terms)

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Built as a hands-on playground for exploring modern monocular depth estimators. Adjust tabs, compare outputs, and plug results into your 3D workflows.