DIODE: A Dense Indoor and Outdoor DEpth Dataset

Authors: Igor Vasiljevic, Nick Kolkin, Shanyi Zhang, Ruotian Luo, Haochen Wang, Falcon Z. Dai, Andrea F. Daniele, Mohammadreza Mostajabi, Steven Basart, Matthew R. Walter, Gregory Shakhnarovich  from TTI-Chicago.

Introduction

DIODE (Dense Indoor and Outdoor DEpth) is a dataset that contains diverse high-resolution color images with accurate, dense, wide-range depth measurements. It is the first public dataset to include RGBD images of indoor and outdoor scenes obtained with one sensor suite. For more information, please refer to our technical report.

News

  • August 1st, 2019 : DIODE initial release.

  • DIODE Dataset

    Dataset Download

    We have released the train and val splits of DIODE, including RGB images, depth maps and depth validity masks. Test set and surface normals are coming soon.

    Download links:

    Dataset Layout

    DIODE data is organized hierarchically.

    Description: A 'scene' usually corresponds to a somewhat compact location/vicinity, such as interior (or a single floor) of a building, surroundings of a landmark, neighborhood, etc. A 'scan' corresponds to a single data acquisition by the scanner, resulting in a set of crops, all taken from the same position. Multiple scans within the same scene may or may not have overlap in the physical points they capture; scans in distinct scenes will typically have no overlap. Note that we use the name of "indoors" and "outdoor" to keep the characters constant.

    File Naming Conventions and Formats

    The dataset consists of RGB images, depth maps and depth validity masks. Their formats are as follows:

    RGB images (*.png): RGB images with a resolution of 1024 × 768.

    Depth maps (*_depth.npy): Depth ground truth with the same resolution as the images.

    Depth masks (*_depth_mask.npy): Binary depth validity masks where 1 indicates valid sensor returns and 0 otherwise.

    Dataset Feature

    Dataset Statistics


    Baseline Performance

    Here we provide the baseline performance of single image depth estimation on the DIODE dataset. Please refer to Densedepth and our upcoming v2 paper for more detail.


    DIODE Development Toolkit

    Please visit our official project repository for more information and DIODE development toolkit.
    Devkit Link: diode-devkit


    License

    The DIODE dataset and the code is released using the MIT license.


    Citation

    If you use the DIODE dataset please cite:

    @article{diode_dataset,
      title={{DIODE}: {A} {D}ense {I}ndoor and {O}utdoor {DE}pth {D}ataset},
      author={Igor Vasiljevic and Nick Kolkin and Shanyi Zhang and Ruotian Luo and
      Haochen Wang and Falcon Z. Dai and Andrea F. Daniele and Mohammadreza Mostajabi and
      Steven Basart and Matthew R. Walter and Gregory Shakhnarovich},
      journal={CoRR},
      volume={abs/1908.00463},
      year={2019},
      url={http://arxiv.org/abs/1908.00463}
    }
    


    Contact

    If you have any questions, please contact us at diode.dataset@gmail.com.


    Acknowledgements

    This research was in part sponsored by:





    Last updated: August 18th, 2019