ScanNet: Richly-Annotated 3D Reconstructions of Indoor Scenes

@article{Dai2017ScanNetR3,
  title={ScanNet: Richly-Annotated 3D Reconstructions of Indoor Scenes},
  author={Angela Dai and Angel X. Chang and Manolis Savva and Maciej Halber and Thomas A. Funkhouser and Matthias Nie\ssner},
  journal={2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},
  year={2017},
  pages={2432-2443}
}
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… CONTINUE READING
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