ScanNet: Richly-Annotated 3D Reconstructions of Indoor Scenes
Abstract
A key requirement for leveraging supervised deep learning methods is the availability of large, labeled datasets. Unfortunately, in the context of RGB-D scene understanding, very little data is available - current datasets cover a small range of scene views and have limited semantic annotations. To address this issue, we introduce ScanNet, an RGB-D video dataset containing 2.5M views in 1513 scenes annotated with 3D camera poses, surface reconstructions, and semantic segmentations. To collect this data, we designed an easy-to-use and scalable RGB-D capture system that includes automated surface reconstruction and crowd-sourced semantic annotation.We show that using this data helps achieve state-of-the-art performance on several 3D scene understanding tasks, including 3D object classification, semantic voxel labeling, and CAD model retrieval.
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The videos in VSI-Bench are sourced from the validation sets of ScanNet, ScanNet++, and ARKitScenes.
“These videos are sourced from the validation sets of the public indoor 3D scene reconstruction datasets ScanNet [19], ScanNet++ [97], and ARKitScenes [5]”
From Thinking in Space: How Multimodal Large Language Models See, Remember, and Recall Spaces (2025) · cites this paper as [19]
The benchmark aggregates existing 3D indoor scene understanding and reconstruction datasets: ScanNet, ScanNet++, and ARKitScenes.
“Our benchmark aggregates existing 3D indoor scene understanding and reconstruction datasets: ScanNet [19], ScanNet++ [97], and ARKitScenes [5].”
From Thinking in Space: How Multimodal Large Language Models See, Remember, and Recall Spaces (2025) · cites this paper as [19]
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- Authors
- Dai, A., Chang, A. X., Savva, M., Halber, M., Funkhouser, T., & Niessner, M.
- Affiliations
- Stanford University (US)
- Princeton University (US)
- Venue
- 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR)
- Publisher
- IEEE
- Published
- 2017-07
- Pages
- 2432-2443
- ISSN
- 1063-6919 (ISSN-L)
- OpenAlex
- W2594519801
- Cited by
- 4,368
- References
- 7
- Citations by year
- 2026: 495 · 2025: 830 · 2024: 832 · 2023: 713 · 2022: 456 · 2021: 432 · 2020: 301 · 2019: 209 · 2018: 82 · 2017: 18
- FWCI
- 251.10
- Type
- conference paper
- Language
- en
- Open access
- closed
- Locations
- IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings (publishedVersion)
- Funders
- National Science Foundation (1539099, 1539014, 1251217)
- Nvidia
- Topics
- Physical Sciences › Engineering › Computational Mechanics › 3D Shape Modeling and Analysis
- Physical Sciences › Earth and Planetary Sciences › Geology › 3D Surveying and Cultural Heritage
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Advanced Vision and Imaging
- Keywords
- ScanNet, RGB-D dataset, indoor scenes, 3D scene understanding, semantic segmentation, surface reconstruction, 3D object classification, CAD model retrieval, crowdsourced annotation
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