ETH3D Depth Dataset

ETH3D is a high-resolution multi-view-stereo benchmark used for monocular depth estimation. It provides survey-grade laser-scanner ground truth across both indoor and outdoor scenes.

Key Features

• Survey-grade depth ground truth captured with a high-precision laser scanner.
• High-resolution images covering both indoor and outdoor scenes.
• Depth range up to approximately 60 m.
• Evaluation is performed on 423 images.
• A high-quality multi-view-stereo benchmark with accurate dense ground truth.

Role in YOLO26-Depth

ETH3D is a zero-shot evaluation benchmark for the YOLO26-Depth family; the published models are not trained on it. Its mix of indoor and outdoor scenes with accurate laser-scanner ground truth makes it a strong test of cross-domain generalization.

Evaluation uses multi-scale and horizontal-flip test-time augmentation (TTA), followed by log-least-squares scale alignment between the predicted and ground-truth depth maps before metrics are computed.

Results

The table below reports the delta1 accuracy (percentage of pixels within a 1.25× threshold, higher is better) on the ETH3D evaluation images by model size.

Evaluation

ETH3D is not shipped with a bundled dataset YAML. It is evaluated through a dedicated evaluation script that loads the ETH3D images and laser-scanner depth ground truth, applies the standard TTA and log-least-squares scale alignment, and reports the depth metrics.

Usage

ETH3D is an external benchmark, so models are typically run with predict on its images. For a comprehensive list of available arguments, refer to the model Prediction page.

!!! example "Predict Example"

=== "Python"

    ```python
    from ultralytics import YOLO

    # Load a model
    model = YOLO("yolo26x-depth.pt")  # load a pretrained depth model

    # Predict depth on ETH3D images
    results = model.predict("path/to/eth3d/images")
    ```

=== "CLI"

    ```bash
    # Predict depth with a pretrained *.pt model
    yolo depth predict model=yolo26x-depth.pt source=path/to/eth3d/images
    ```

Pretrained Models

The YOLO26 depth family is evaluated zero-shot on the ETH3D benchmark. These models auto-download from the latest Ultralytics release, for example YOLO26x-depth from v8.4.0, and span a range of sizes (yolo26n/s/m/l/x-depth) for different accuracy and resource requirements.

Citations and Acknowledgments

If you use the ETH3D dataset in your research or development work, please cite the following paper:

!!! quote ""

=== "BibTeX"

    ```bibtex
    @inproceedings{schops2017eth3d,
          title={A Multi-View Stereo Benchmark with High-Resolution Images and Multi-Camera Videos},
          author={Sch{\"o}ps, Thomas and Sch{\"o}nberger, Johannes L. and Galliani, Silvano and Sattler, Torsten and Schindler, Konrad and Pollefeys, Marc and Geiger, Andreas},
          booktitle={Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},
          year={2017}
    }
    ```

We would like to acknowledge the authors for creating and maintaining this valuable resource for the computer vision community.