the hypersim dataset

We have hosted the application the hypersim dataset in order to run this application in our online workstations with Wine or directly.

Quick description about the hypersim dataset:

Hypersim is a large-scale, photorealistic synthetic dataset and tooling suite for indoor scene understanding research. It provides richly annotated renderings�RGB, depth, surface normals, instance and semantic segmentations, and material/lighting metadata�produced from high-fidelity virtual environments. The dataset spans diverse furniture layouts, room types, and camera trajectories, enabling robust training for geometry, segmentation, and SLAM-adjacent tasks. Rendering pipelines and utilities allow researchers to reproduce sequences, generate novel views, or extract task-specific supervision. Because the data are perfectly labeled and controllable, Hypersim is well suited for pretraining and for studying domain transfer to real imagery. The repository acts as both a dataset index and a set of scripts for downloading, managing, and evaluating on standardized splits.

Features:

• Photorealistic indoor scenes with multi-modal annotations
• Depth, normals, instance/semantic masks, and materials/lighting
• Reproducible rendering pipelines and camera trajectories
• Tools for dataset download, indexing, and standardized splits
• Strong pretraining source for geometry and segmentation tasks
• Controls for viewpoint and scene generation to study generalization

Programming Language: Python.
Categories: