The Habitat-Matterport 3D Semantics Dataset (HM3DSem) is the largest-ever dataset of 3D real-world and indoor spaces with densely annotated semantics that is available to the academic community. HM3DSem v0.2 consists of 142,646 object instance annotations across 216 3D-spaces from HM3D and 3,100 rooms within those spaces. The HM3D scenes are annotated with the 142,646 raw object names, which are mapped to 40 Matterport categories. On average, each scene in HM3DSem v0.2 consists of 661 objects from 106 categories. This dataset is the result of 14,200+ hours of human effort for annotation and verification by 20+ annotators.
HM3DSem v0.2 is free and available here for academic, non-commercial research. Researchers can use it with FAIR’s Habitat simulator to train embodied agents, such as home robots and AI assistants, at scale for semantic navigation tasks. HM3DSem v0.1 was also the basis of the recently concluded Habitat 2022 ObjectNav challenge. Please see our arxiv report for more details.
Citing HM3DSem
If you use the HM3DSem dataset in your research, please cite the following:
Habitat-Matterport 3D Semantics Dataset
@article{yadav2022habitat,
title={Habitat-Matterport 3D Semantics Dataset},
author={Yadav, Karmesh and Ramrakhya, Ram and Ramakrishnan, Santhosh Kumar and Gervet, Theo and Turner, John and Gokaslan, Aaron and Maestre, Noah and Chang, Angel Xuan and Batra, Dhruv and Savva, Manolis and others},
journal={arXiv preprint arXiv:2210.05633},
year={2022},
url={https://arxiv.org/abs/2210.05633}
}
Habitat-Matterport 3D Dataset
@inproceedings{ramakrishnan2021hm3d,
title={Habitat-Matterport 3D Dataset ({HM}3D): 1000 Large-scale 3D Environments for Embodied {AI}},
author={Santhosh Kumar Ramakrishnan and Aaron Gokaslan and Erik Wijmans and Oleksandr Maksymets and Alexander Clegg and John M Turner and Eric Undersander and Wojciech Galuba and Andrew Westbury and Angel X Chang and Manolis Savva and Yili Zhao and Dhruv Batra},
booktitle={Thirty-fifth Conference on Neural Information Processing Systems Datasets and Benchmarks Track (Round 2)},
year={2021},
url={https://openreview.net/forum?id=-v4OuqNs5P}
}