Link to this sectionMake3D Depth Dataset#

Make3D is a classic outdoor benchmark for monocular depth estimation. It contains images of campus scenes paired with depth ground truth captured by a custom 3D laser scanner, and is widely used to probe out-of-distribution generalization.

Link to this sectionKey Features#

• Depth ground truth captured with a custom 3D laser scanner.
• Covers real outdoor campus scenes.
• Depth range up to approximately 70 m.
• Evaluation is performed on the standard test set of 134 images.
• A classic out-of-distribution generalization benchmark.

Link to this sectionRole in YOLO26-Depth#

Make3D is a zero-shot evaluation benchmark for the YOLO26-Depth family; the published models are not trained on it. As an out-of-distribution outdoor set, it is the hardest benchmark for all models, and absolute delta1 values are low across the board, which is expected for this dataset.

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.

Link to this sectionResults#

The table below reports the delta1 accuracy (percentage of pixels within a 1.25× threshold, higher is better) on the Make3D test set by model size.

Link to this sectionEvaluation#

Make3D is not shipped with a bundled dataset YAML. It is evaluated through a dedicated evaluation script that loads the Make3D images and laser-scanner depth ground truth, applies the standard TTA and log-least-squares scale alignment, and reports the depth metrics.

Link to this sectionUsage#

Make3D 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.

from ultralytics import YOLO

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

# Predict depth on Make3D images
results = model.predict("path/to/make3d/images")

Link to this sectionPretrained Models#

The YOLO26 depth family is evaluated zero-shot on the Make3D 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.

Link to this sectionCitations and Acknowledgments#

If you use the Make3D dataset in your research or development work, please cite the following paper:

@article{saxena2009make3d,
      title={Make3D: Learning 3D Scene Structure from a Single Still Image},
      author={Saxena, Ashutosh and Sun, Min and Ng, Andrew Y.},
      journal={IEEE Transactions on Pattern Analysis and Machine Intelligence (TPAMI)},
      year={2009}
}

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