ScanNet: Richly-annotated 3D Reconstructions of Indoor Scenes

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. We show that using this data helps achieve state-of-the-art performance on several 3D scene understanding tasks, including 3D object classification, semantic voxel labeling, and CAD model retrieval. The dataset is freely available at

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Results from the Paper

Task Dataset Model Metric Name Metric Value Global Rank Benchmark
Semantic Segmentation ScanNet ScanNet 3DIoU 0.306 # 21
Semantic Segmentation ScanNetV2 ScanNet (2d proj) Mean IoU 33.0% # 9


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