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Cited by 3583D Shape Modeling and AnalysisAdvanced Neural Network ApplicationsDomain Adaptation and Few-Shot Learning

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 access
Roberts, Mike, Ramapuram, Jason, Ranjan, Anurag, Kumar, Atulit, Bautista, Miguel Angel, Paczan, Nathan, Webb, Russ, Susskind, Joshua M.·2021·2021 IEEE/CVF International Conference on Computer Vision (ICCV)
DOI 10.1109/iccv48922.2021.01073
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Abstract

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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Crossref · 10.1109/iccv48922.2021.01073

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Research works printed but not linked: 2

  1. jin, 0, Geometric structure based and regularized depth estimation from 360 degree indoor imagery, CVPR 2020
  2. xiang, 0, A large scale database for 3D object recognition, ECC 2016

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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
DOI
10.1109/iccv48922.2021.01073
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
Licence
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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