The Hypersim Dataset
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 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. Together, these features make our dataset well-suited for geometric learning problems that require direct 3D supervision, multi-task learning problems that require reasoning jointly over multiple input and output modalities, and inverse rendering problems.
The Hypersim Dataset is licensed under the Creative Commons Attribution-ShareAlike 3.0 Unported License.