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

Chandan Yeshwanth*, Yueh-Cheng Liu*, Matthias Nießner, Angela Dai
Technical University of Munich
*equal contribution
ICCV 2023 Oral [Paper]
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News

v1_v2


  • December 20, 2024: ScanNet++ v2 released with 🔥1000+ scenes🔥, more scene types, updated annotations and poses. Check out the Changelog for details
  • November 2023: Version 1 of the dataset and NVS and semantic benchmarks released, several updates to the dataset.
  • October 2023: A ready-to-run dataparser for ScanNet++ is in Nerfstudio now.
  • September 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 1000+ 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

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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

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Privacy

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