ScanNet++: A High-Fidelity Dataset of 3D Indoor Scenes

Technical University of Munich
*equal contribution


  • November 23, 2023: NVS and Semantic benchmarks released, several updates to the dataset. Check out the Changelog for details
  • October 12, 2023: A ready-to-run dataparser for ScanNet++ is in Nerfstudio now.
  • September 28, 2023: ScanNet++ website is up! Apply for access to download the data now.🔥

Download the data

To download the data, please create an account, login and create an application. Once your application is approved, you will receive a personalized token to download the data along with further instructions.


ScanNet++ is a large scale dataset with 450+ 3D indoor scenes containing sub-millimeter resolution laser scans, registered 33-megapixel DSLR images, and commodity RGB-D streams from iPhone. The 3D reconstructions are annotated with long-tail and label-ambiguous semantics to benchmark semantic understanding methods, while the coupled DSLR and iPhone captures enable benchmarking of novel view synthesis methods in high-quality and commodity settings.




If you use the ScanNet++ data or code please cite:

  title={ScanNet++: A High-Fidelity Dataset of 3D Indoor Scenes},
  author={Yeshwanth, Chandan and Liu, Yueh-Cheng and Nie{\ss}ner, Matthias and Dai, Angela},
  booktitle = {Proceedings of the International Conference on Computer Vision ({ICCV})},


The ScanNet++ data is released under the ScanNet++ Terms of Use, which you can agree to after signing up.


We take privacy very seriously. We have taken great care to ensure that the data is anonymized and does not contain any personally identifiable information. If you notice any privacy concerns, please contact us.