ScanNet: Richly-annotated 3D Reconstructions of Indoor Scenes

Understand

A key requirement for leveraging supervised deep learning methods is the availability of large, labeled datasets.

• Unfortunately, in the context of RGB-D scene understanding, very little data is available -- current datasets cover a small range of scene views and have limited semantic annotations.
• To address this issue, we introduce ScanNet, an RGB-D video dataset containing 2.5M views in 1513 scenes annotated with 3D camera poses, surface reconstructions, and semantic segmentations.
• To collect this data, we designed an easy-to-use and scalable RGB-D capture system that includes automated surface reconstruction and crowdsourced semantic annotation.