ScanNet: Richly-annotated 3D reconstructions of indoor scenes

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

Publikation: Beitrag in Buch/Bericht/KonferenzbandKonferenzbeitragBegutachtung

1878 Zitate (Scopus)

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.

OriginalspracheEnglisch
TitelProceedings - 30th IEEE Conference on Computer Vision and Pattern Recognition, CVPR 2017
Herausgeber (Verlag)Institute of Electrical and Electronics Engineers Inc.
Seiten2432-2443
Seitenumfang12
ISBN (elektronisch)9781538604571
DOIs
PublikationsstatusVeröffentlicht - 6 Nov. 2017
Veranstaltung30th IEEE Conference on Computer Vision and Pattern Recognition, CVPR 2017 - Honolulu, USA/Vereinigte Staaten
Dauer: 21 Juli 201726 Juli 2017

Publikationsreihe

NameProceedings - 30th IEEE Conference on Computer Vision and Pattern Recognition, CVPR 2017
Band2017-January

Konferenz

Konferenz30th IEEE Conference on Computer Vision and Pattern Recognition, CVPR 2017
Land/GebietUSA/Vereinigte Staaten
OrtHonolulu
Zeitraum21/07/1726/07/17

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