ScanNet: Richly-annotated 3D Reconstructions of Indoor Scenes

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ScanNet: Richly-annotated 3D Reconstructions of Indoor Scenes

ScanNet is a dataset of richly-annotated RGB-D scans of real-world environments containing 2.5M RGB-D images in more than 1500 scans, annotated with 3D camera poses, surface reconstructions, and instance-level semantic segmentations. (Angela Dai, Angel X. Chang, Manolis Savva, Maciej Halber, Thomas Funkhouser, Matthias Niessner)

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