ScanNet: Richly-Annotated 3D Reconstructions of Indoor Scenes

作者：Angela Dai, Anne Lynn S. Chang, Manolis Savva, Maciej Halber, Thomas A. Funkhouser, Matthias Nießner · 年份：2017 · DOI：10.1109/cvpr.2017.261 · 被引用次数：4281 · 研究领域：3D Surveying and Cultural Heritage、Robotics and Sensor-Based Localization、Remote Sensing and LiDAR Applications

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 crowd-sourced 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.