IRS: A Large Naturalistic Indoor Robotics Stereo Dataset to Train Deep Models for Disparity and Surface Normal Estimation

Qiang Wang, Shizhen Zheng, Qingsong Yan, Fei Deng, Kaiyong Zhao, Xiaowen Chu

Research output: Chapter in book/report/conference proceedingConference contributionpeer-review

Abstract

Indoor robotics applications heavily rely on scene understanding and reconstruction. Compared to monocular vision, stereo vision methods are more promising to produce accurate geometrical information, such as surface normal and depth/disparity. Besides, deep learning models have shown their superior performance in stereo vision tasks. However, existing stereo datasets rarely contain high-quality surface normal and disparity ground truth, hardly satisfying the demand of training a prospective deep model. To this end, we introduce a large-scale indoor robotics stereo (IRS) dataset with over 100K stereo images and high-quality surface normal and disparity maps. Leveraging the advanced techniques of our customized rendering engine, the dataset is considerably close to the real-world scenes. Besides, we present DTN-Net, a two-stage deep model for surface normal estimation. Extensive experiments show the advantages and effectiveness of IRS in training deep models for disparity estimation, and DTN-Net provides state-of-the-art results for normal estimation compared to existing methods.
Original languageEnglish
Title of host publication2021 IEEE International Conference on Multimedia and Expo (ICME)
PublisherIEEE
Pages1-6
Number of pages6
ISBN (Electronic)9781665438643
ISBN (Print)9781665411523
DOIs
Publication statusPublished - 9 Jul 2021
Event2021 IEEE International Conference on Multimedia and Expo, ICME 2021 - Shenzhen, China
Duration: 5 Jul 20219 Jul 2021

Publication series

NameProceedings of IEEE International Conference on Multimedia and Expo (ICME)
ISSN (Print)1945-7871
ISSN (Electronic)1945-788X

Conference

Conference2021 IEEE International Conference on Multimedia and Expo, ICME 2021
Country/TerritoryChina
CityShenzhen
Period5/07/219/07/21

User-Defined Keywords

  • Training
  • Surface reconstruction
  • Multimedia systems
  • Refining
  • Estimation
  • Rendering (computer graphics)
  • Stereo vision

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