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arXiv · 1702.04405

ScanNet: Richly-annotated 3D Reconstructions of Indoor Scenes

Angela Dai·Angel X. Chang·Manolis Savva·Maciej Halber·Thomas Funkhouser·Matthias Nießner
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Abstract

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 http://www.scan-net.org.

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Angela Dai, Angel X. Chang, Manolis Savva, Maciej Halber, Thomas Funkhouser, Matthias Nießner. 2017-02-14. ScanNet: Richly-annotated 3D Reconstructions of Indoor Scenes. https://arxiv.org/abs/1702.04405

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