FuryTMP/Distill-Any-Depth-Small-onnx

Distill-Any-Depth-Small (ONNX) – For VisionDepth3D

Model Origin: This model is based on Distill-Any-Depth by Westlake-AGI-Lab, originally developed by Westlake-AGI-Lab.
I did not train this model — I have converted it to ONNX format for fast, GPU-accelerated inference within tools such as VisionDepth3D.

🧠 About This Model

This is a direct conversion of the Distill-Any-Depth PyTorch model to ONNX, intended for lightweight, real-time depth estimation from single RGB images.

✔️ Key Features:

• ONNX format (exported from PyTorch)
• Compatible with ONNX Runtime and TensorRT
• Excellent for 2D to 3D depth workflows
• Works seamlessly with VisionDepth3D

📌 Intended Use

• Real-time or batch depth map generation
• 2D to 3D conversion pipelines (e.g., SBS 3D video)
• Works on Windows, Linux (CUDA-supported)

📜 License and Attribution

Citation

@article{he2025distill,
  title   = {Distill Any Depth: Distillation Creates a Stronger Monocular Depth Estimator},
  author  = {Xiankang He and Dongyan Guo and Hongji Li and Ruibo Li and Ying Cui and Chi Zhang},
  year    = {2025},
  journal = {arXiv preprint arXiv: 2502.19204}
}

• Source Model: Distill-Any-Depth by Westlake-AGI-Lab
• License: MIT
• Modifications: Only format conversion (no retraining or weight changes)

If you use this model, please credit the original authors: Westlake-AGI-Lab.

💻 How to Use In VisionDepth3D

Place Folder containing onnx model into weights folder in VisionDepth3D

VisionDepth3D¬
              Weights¬
                      Distill Any Depth Small¬
                                            model.onnx