Matterport3D: Learning from RGB-D data in indoor environments

Angel Chang, Angela Dai, Thomas Funkhouser, Maciej Halber, Matthias Niebner, Manolis Savva, Shuran Song, Andy Zeng, Yinda Zhang

Research output: Chapter in Book/Report/Conference proceedingConference contribution

113 Scopus citations

Abstract

Access to large, diverse RGB-D datasets is critical for training RGB-D scene understanding algorithms. However, existing datasets still cover only a limited number of views or a restricted scale of spaces. In this paper, we introduce Matterport3D, a large-scale RGB-D dataset containing 10,800 panoramic views from 194,400 RGB-D images of 90 building-scale scenes. Annotations are provided with surface reconstructions, camera poses, and 2D and 3D semantic segmentations. The precise global alignment and comprehensive, diverse panoramic set of views over entire buildings enable a variety of supervised and self-supervised computer vision tasks, including keypoint matching, view overlap prediction, normal prediction from color, semantic segmentation, and region classification.

Original languageEnglish (US)
Title of host publicationProceedings - 2017 International Conference on 3D Vision, 3DV 2017
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages667-676
Number of pages10
ISBN (Electronic)9781538626108
DOIs
StatePublished - May 25 2018
Event7th IEEE International Conference on 3D Vision, 3DV 2017 - Qingdao, China
Duration: Oct 10 2017Oct 12 2017

Publication series

NameProceedings - 2017 International Conference on 3D Vision, 3DV 2017

Other

Other7th IEEE International Conference on 3D Vision, 3DV 2017
CountryChina
CityQingdao
Period10/10/1710/12/17

All Science Journal Classification (ASJC) codes

  • Media Technology
  • Computer Vision and Pattern Recognition
  • Signal Processing

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