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SUN RGB-D: A RGB-D scene understanding benchmark suite
Abstract
Although RGB-D sensors have enabled major break-throughs for several vision tasks, such as 3D reconstruction, we have not attained the same level of success in high-level scene understanding. Perhaps one of the main reasons is the lack of a large-scale benchmark with 3D annotations and 3D evaluation metrics. In this paper, we introduce an RGB-D benchmark suite for the goal of advancing the state-of-the-arts in all major scene understanding tasks. Our dataset is captured by four different sensors and contains 10,335 RGB-D images, at a similar scale as PASCAL VOC. The whole dataset is densely annotated and includes 146,617 2D polygons and 64,595 3D bounding boxes with accurate object orientations, as well as a 3D room layout and scene category for each image. This dataset enables us to train data-hungry algorithms for scene-understanding tasks, evaluate them using meaningful 3D metrics, avoid overfitting to a small testing set, and study cross-sensor bias.
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“with Gemini Robotics-ER achieving a new stateof-the-art on the SUN-RGBD benchmark.”
From Gemini Robotics: Bringing AI into the Physical World (2025) · cites this paper as Song et al., 2015
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“Specifically, on the SUN RGB-D dataset (Song et al., 2015), the Qwen3-VL-235B-A22B-Thinking variants surpass the performance of Gemini-2.5-Proby 5.2 points.”
From Qwen3-VL Technical Report (2025) · cites this paper as Song et al., 2015
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Metadata
- Authors
- Song, S., Lichtenberg, S. P., & Xiao, J.
- Affiliations
- Princeton University (US)
- Venue
- 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR)
- Publisher
- IEEE
- Published
- 2015-06
- Pages
- 567-576
- ISSN
- 1063-6919 (ISSN-L)
- OpenAlex
- W1923184257
- Cited by
- 1,943
- References
- 74
- Citations by year
- 2026: 117 · 2025: 232 · 2024: 266 · 2023: 262 · 2022: 223 · 2021: 238 · 2020: 162 · 2019: 172 · 2018: 119 · 2017: 98 · 2016: 50 · 2015: 4
- FWCI
- 37.05
- 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)
- Topics
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Advanced Neural Network Applications
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Advanced Vision and Imaging
- Physical Sciences › Computer Science › Computer Graphics and Computer-Aided Design › 3D Shape Modeling and Analysis
- Keywords
- benchmark dataset, 3D annotation, 3D bounding boxes
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
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