360MonoDepth: High-Resolution 360° Monocular Depth Estimation

Manuel Rey-Area, Mingze Yuan, Christian Richardt

Research output: Chapter or section in a book/report/conference proceedingChapter in a published conference proceeding

25 Citations (SciVal)
114 Downloads (Pure)

Abstract

360° cameras can capture complete environments in a single shot, which makes 360° imagery alluring in many computer vision tasks. However, monocular depth estimation remains a challenge for 360° data, particularly for high resolutions like 2K (2048 × 1 024) and beyond that are important for novel-view synthesis and virtual reality applications. Current CNN-based methods do not support such high resolutions due to limited GPU memory. In this work, we propose aflexible framework for monocular depth estimation from high-resolution 360° images using tangent images. We project the 360° input image onto a set of tangent planes that produce perspective views, which are suitable for the latest, most accurate state-of-the-art perspective monocular depth estimators. To achieve globally consistent disparity estimates, we recombine the individual depth estimates using deformable multi-scale alignment followed by gradient-domain blending. The result is a dense, high-resolution 360° depth map with a high level of detail, also for outdoor scenes which are not supported by existing methods. Our source code and data are available at https://manurare.github.io/360monodepth/.

Original languageEnglish
Title of host publicationProceedings - 2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition, CVPR 2022
PublisherIEEE
Pages3752-3762
Number of pages11
ISBN (Electronic)978-1-6654-6946-3
DOIs
Publication statusPublished - 27 Sept 2022

Publication series

NameProceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition
Volume2022-June
ISSN (Print)1063-6919

Keywords

  • 3D from single images

ASJC Scopus subject areas

  • Software
  • Computer Vision and Pattern Recognition

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