The DOI matches its Crossref record; 88 of the 95 references it cites were found in a registry or an index.
Hypersim: A Photorealistic Synthetic Dataset for Holistic Indoor Scene Understanding
Open accessAbstract
For many fundamental scene understanding tasks, it is difficult or impossible to obtain per-pixel ground truth labels from real images. We address this challenge by introducing Hypersim, a photorealistic synthetic dataset for holistic indoor scene understanding. To create our dataset, we leverage a large repository of synthetic scenes created by professional artists, and we generate 77,400 images of 461 indoor scenes with detailed per-pixel labels and corresponding ground truth geometry. Our dataset: (1) relies exclusively on publicly available 3D assets; (2) includes complete scene geometry, material information, and lighting information for every scene; (3) includes dense per-pixel semantic instance segmentations and complete camera information for every image; and (4) factors every image into diffuse reflectance, diffuse illumination, and a non-diffuse residual term that captures view-dependent lighting effects.We analyze our dataset at the level of scenes, objects, and pixels, and we analyze costs in terms of money, computation time, and annotation effort. Remarkably, we find that it is possible to generate our entire dataset from scratch, for roughly half the cost of training a popular open-source natural language processing model. We also evaluate sim-to-real transfer performance on two real-world scene understanding tasks – semantic segmentation and 3D shape prediction – where we find that pre-training on our dataset significantly improves performance on both tasks, and achieves state-of-the-art performance on the most challenging Pix3D test set. All of our rendered image data, as well as all the code we used to generate our dataset and perform our experiments, is available online.
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“VisGel [246], leveraging GelSight sensors, constructs bidirectional generation tasks between tactile and visual modalities, further validating the feasibility of inter-modal information transformation.”
From Multimodal fusion and vision–language models: A survey for robot vision (2026) · cites this paper as [246]
Citations
Cites
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- jin, 0, Geometric structure based and regularized depth estimation from 360 degree indoor imagery, CVPR 2020
- xiang, 0, A large scale database for 3D object recognition, ECC 2016
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Kirillov, A., Mintun, E., Ravi, N., Mao, H., Rolland, C., Gustafson, L., Xiao, T., Whitehead, S., Berg, A. C., Lo, W.-Y., et al. (2023) · 2023 IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv51070.2023.00371 · Cited by 11,953
“ADE20K[117] BBBC038v1[12] Cityscapes[25] DOORS[80] DRAM[24] EgoHOS[113] GTEA[34,63] Hypersim[86] IBD[17] iShape[111] LVIS[44] NDD20[100] NDISPark[22,23] OVIS[81] PPDLS[74] Plittersdorf[46] STREETS[91] TimberSeg[38] TrashCan[52] VISOR[28,27] WoodScape[112] PIDRay[104] ZeroWaste-f[6] Figure 8: Samples from the 23 divers…”
BackgroundInfluentialDepth Anything: Unleashing the Power of Large-Scale Unlabeled Data
Yang, L., Kang, B., Huang, Z., Xu, X., Feng, J., & Zhao, H. (2024) · 2024 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr52733.2024.00987 · Cited by 1,296
Data augmentation: A comprehensive survey of modern approaches
Mumuni, A., & Mumuni, F. (2022) · Array · doi:10.1016/j.array.2022.100258 · Cited by 891
Repurposing Diffusion-Based Image Generators for Monocular Depth Estimation
Ke, B., Obukhov, A., Huang, S., Metzger, N., Daudt, R. C., & Konrad Schindler, B. (2024) · IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings · doi:10.1109/cvpr52733.2024.00907 · Cited by 595
MultiMAE: Multi-modal Multi-task Masked Autoencoders
Bachmann, R., Mizrahi, D., Atanov, A., & Zamir, A. (2022) · Lecture notes in computer science · doi:10.1007/978-3-031-19836-6_20 · Cited by 268
Metadata
- Authors
- Roberts, M., Ramapuram, J., Ranjan, A., Kumar, A., Bautista, M. A., Paczan, N., Webb, R., & Susskind, J. M.
- Affiliations
- Apple (United States) (US)
- Apple
- ORCID
- Ramapuram, Jason
- Venue
- 2021 IEEE/CVF International Conference on Computer Vision (ICCV)
- Publisher
- IEEE
- Published
- 2021-10
- Pages
- 10892-10902
- OpenAlex
- W3097660860
- Cited by
- 358
- References
- 95
- Citations by year
- 2026: 85 · 2025: 88 · 2024: 73 · 2023: 61 · 2022: 31 · 2021: 19
- FWCI
- 33.65
- Type
- conference paper
- Language
- en
- Open access
- green
- arxiv.org
- Locations
- IEEE/CVF International Conference on Computer Vision (ICCV) (publishedVersion)
- arXiv (Cornell University) (submittedVersion)
- arXiv (Cornell University)
- IEEE/CVF International Conference on Computer Vision (ICCV)
- Topics
- Physical Sciences › Computer Science › Computer Graphics and Computer-Aided Design › 3D Shape Modeling and Analysis
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Advanced Neural Network Applications
- Physical Sciences › Computer Science › Artificial Intelligence › Domain Adaptation and Few-Shot Learning
- Keywords
- synthetic dataset, indoor scene understanding, photorealistic rendering, instance segmentation, sim-to-real transfer, diffuse reflectance, scene geometry, annotation cost
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