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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 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.|
|Citation:||Dai, Angela, Angel X. Chang, Manolis Savva, Maciej Halber, Thomas Funkhouser, and Matthias Niessner. "ScanNet: Richly-Annotated 3D Reconstructions of Indoor Scenes." In IEEE Conference on Computer Vision and Pattern Recognition (CVPR) (2017): pp. 2432-2443. doi:10.1109/CVPR.2017.261|
|Pages:||2432 - 2443|
|Type of Material:||Conference Article|
|Journal/Proceeding Title:||IEEE Conference on Computer Vision and Pattern Recognition|
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