Matterport3D: Learning from RGB-D Data in Indoor Environments
On the map: World models → Video-generation world models and simulators
Abstract (arXiv)
Access to large, diverse RGB-D datasets is critical for training RGB-D scene understanding algorithms. However, existing datasets still cover only a limited number of views or a restricted scale of spaces. In this paper, we introduce Matterport3D, a large-scale RGB-D dataset containing 10,800 panoramic views from 194,400 RGB-D images of 90 building-scale scenes. Annotations are provided with surface reconstructions, camera poses, and 2D and 3D semantic segmentations. The precise global alignment and comprehensive, diverse panoramic set of views over entire buildings enable a variety of supervised and self-supervised computer vision tasks, including keypoint matching, view overlap prediction, normal prediction from color, semantic segmentation, and region classification.
Key facts. Cited by 1,354 papers in the map (data through 7 Oct 2026). Placed in Video-generation world models and simulators, one of 11 directions in World models. Builds on 0 of its 48 references that are in the map. First posted on arXiv 18 Sep 2017.
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Citing papers per year: 2017: 1; 2018: 2; 2019: 2; 2020: 4; 2021: 159; 2022: 188; 2023: 188; 2024: 258; 2025: 267; 2026: 285.
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