Part of Proceedings of the Neural Information Processing Systems Track on Datasets and Benchmarks 1 (NeurIPS Datasets and Benchmarks 2021) round2
Santhosh Kumar Ramakrishnan, Aaron Gokaslan, Erik Wijmans, Oleksandr Maksymets, Alexander Clegg, John Turner, Eric Undersander, Wojciech Galuba, Andrew Westbury, Angel Chang, Manolis Savva, Yili Zhao, Dhruv Batra
We present the Habitat-Matterport 3D (HM3D) dataset. HM3D is a large-scale dataset of 1,000 building-scale 3D reconstructions from a diverse set of real-world locations. Each scene in the dataset consists of a textured 3D mesh reconstruction of interiors such as multi-ﬂoor residences, stores, and other private indoor spaces.HM3D surpasses existing datasets available for academic research in terms of physical scale, completeness of the reconstruction, and visual ﬁdelity. HM3D contains 112.5k m^2 of navigable space, which is 1.4 - 3.7× larger than other building-scale datasets (MP3D, Gibson). When compared to existing photorealistic 3D datasets (Replica, MP3D, Gibson, ScanNet), rendered images from HM3D have 20 - 85% higher visual ﬁdelity w.r.t. counterpart images captured with real cameras, and HM3D meshes have 34 - 91% fewer artifacts due to incomplete surface reconstruction.The increased scale, ﬁdelity, and diversity of HM3D directly impacts the performance of embodied AI agents trained using it. In fact, we ﬁnd that HM3D is ‘pareto optimal’ in the following sense – agents trained to perform PointGoal navigation on HM3D achieve the highest performance regardless of whether they are evaluated on HM3D, Gibson, or MP3D. No similar claim can be made about training on other datasets. HM3D-trained PointNav agents achieve 100% performance on Gibson-test dataset, suggesting that it might be time to retire that episode dataset. The HM3D dataset, analysis code, and pre-trained models are publicly released: https://aihabitat.org/datasets/hm3d/.