SUN RGB-D Depth Dataset

SUN RGB-D is a real-world indoor scene-understanding benchmark captured with four different RGB-D sensors: Intel RealSense, Asus Xtion, and Microsoft Kinect v1 and v2. Its multi-sensor design makes it a valuable source of real indoor depth diversity for monocular depth estimation.

Key Features

• Captured with four different RGB-D sensors (Intel RealSense, Asus Xtion, Microsoft Kinect v1 and v2), providing real multi-sensor variety.
• Covers a broad range of real indoor scenes for scene-understanding research.
• Depth range up to approximately 10 m, typical of consumer indoor RGB-D capture.
• Sensor-derived depth ground truth aligned to the RGB frames.
• Contributes real multi-sensor indoor diversity to the Ultralytics depth pretraining mix.

Dataset Structure

The SUN RGB-D depth dataset is split into two subsets:

1. Train: 9,245 images with paired depth maps for training.
2. Val: 1,090 images with paired depth maps for validation during model training.

Each sample consists of one RGB image and one paired .npy float32 depth map storing per-pixel distances in meters, following the Ultralytics depth dataset format.

Role in YOLO26-Depth

SUN RGB-D is a training source in the Ultralytics YOLO26-Depth multi-dataset pretraining mix of roughly 2.19M image–depth pairs. It contributes real multi-sensor indoor diversity, exposing the model to depth captured across several different consumer RGB-D devices. The resulting models are evaluated on the standard NYU, KITTI, Make3D, ETH3D, and iBims-1 benchmarks.

Dataset YAML

A YAML (Yet Another Markup Language) file is used to define the dataset configuration. It contains information about the dataset's paths, classes, and other relevant information.

!!! example "ultralytics/cfg/datasets/depth-sunrgbd.yaml"

```yaml
--8<-- "ultralytics/cfg/datasets/depth-sunrgbd.yaml"
```

Usage

To train a YOLO26n-depth model on the SUN RGB-D dataset with an image size of 640, you can use the following code snippets. For a comprehensive list of available arguments, refer to the model Training page.

!!! example "Train Example"

=== "Python"

    ```python
    from ultralytics import YOLO

    # Load a model
    model = YOLO("yolo26n-depth.pt")  # load a pretrained depth model (recommended for training)

    # Train the model
    results = model.train(data="depth-sunrgbd.yaml", epochs=100, imgsz=640)
    ```

=== "CLI"

    ```bash
    # Start training from a pretrained *.pt model
    yolo depth train data=depth-sunrgbd.yaml model=yolo26n-depth.pt epochs=100 imgsz=640
    ```

Pretrained Models

The YOLO26 depth family is trained on the broad multi-dataset depth pretraining mix that SUN RGB-D is part of. These models auto-download from the latest Ultralytics release, for example YOLO26x-depth from v8.4.0, and span a range of sizes for different accuracy and resource requirements.

Citations and Acknowledgments

If you use the SUN RGB-D dataset in your research or development work, please cite the following paper:

!!! quote ""

=== "BibTeX"

    ```bibtex
    @inproceedings{song2015sunrgbd,
          title={SUN RGB-D: A RGB-D Scene Understanding Benchmark Suite},
          author={Song, Shuran and Lichtenberg, Samuel P. and Xiao, Jianxiong},
          booktitle={Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},
          year={2015}
    }
    ```

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