In-Plane Rotation-Aware Monocular Depth Estimation Using SLAM

Yuki Saito, Ryo Hachiuma, Masahiro Yamaguchi, Hideo Saito

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

1 Citation (Scopus)

Abstract

Estimating accurate depth from an RGB image in any environment is challenging task in computer vision. Recent learning based method using deep Convolutional Neural Networks (CNNs) have driven plausible appearance, but these conventional methods are not good at estimating scenes that have a pure rotation of camera, such as in-plane rolling. This movement imposes perturbations on learning-based methods because gravity direction is considered to be strong prior to CNN depth estimation (i.e., the top region of an image has a relatively large depth, whereas bottom region tends to have a small depth). To overcome this crucial weakness in depth estimation with CNN, we propose a simple but effective refining method that incorporates in-plane roll alignment using camera poses of monocular Simultaneous Localization and Mapping (SLAM). For the experiment, we used public datasets and also created our own dataset composed of mostly in-plane roll camera movements. Evaluation results on these datasets show the effectiveness of our approach.

Original languageEnglish
Title of host publicationFrontiers of Computer Vision - 26th International Workshop, IW-FCV 2020, Revised Selected Papers
EditorsWataru Ohyama, Soon Ki Jung
PublisherSpringer
Pages305-317
Number of pages13
ISBN (Print)9789811548178
DOIs
Publication statusPublished - 2020
EventInternational Workshop on Frontiers of Computer Vision, IW-FCV 2020 - Ibusuki, Japan
Duration: 2020 Feb 202020 Feb 22

Publication series

NameCommunications in Computer and Information Science
Volume1212 CCIS
ISSN (Print)1865-0929
ISSN (Electronic)1865-0937

Conference

ConferenceInternational Workshop on Frontiers of Computer Vision, IW-FCV 2020
CountryJapan
CityIbusuki
Period20/2/2020/2/22

Keywords

  • Convolutional Neural Network
  • Monocular depth estimation
  • Simultaneous Localization and Mapping

ASJC Scopus subject areas

  • Computer Science(all)
  • Mathematics(all)

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