metadata

license: mit
tags:
  - depth-estimation
  - onnx
  - computer-vision
  - visiondepth3d
  - mit-license

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