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

Chandan Yeshwanth^*_, Yueh-Cheng Liu^*_, Matthias Nießner, Angela Dai

Technical University of Munich

*equal contribution

News

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

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Introduction

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.

Benchmarks

Citation

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


@inproceedings{yeshwanthliu2023scannetpp,
  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})},
  year={2023}
}

License

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

Privacy

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.