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Hypersim

A Photorealistic Synthetic Dataset for Holistic Indoor Scene Understanding

Hypersim

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.

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Task
Object Detection
Annotation Types
Bounding Boxes
77400
Items
461
Classes
77400
Labels
Models using this dataset
Last updated on 
January 20, 2022
Licensed under 
CC-BY-SA
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