Estimating image depth using shape collections

Hao Su, Qixing Huang, Niloy Mitra, Yangyan Li, Leonidas Guibas

Research output: Contribution to journalConference articlepeer-review

61 Scopus citations

Abstract

Images, while easy to acquire, view, publish, and share, they lack critical depth information. This poses a serious bottleneck for many image manipulation, editing, and retrieval tasks. In this paper we consider the problem of adding depth to an image of an object, effectively 'lifting' it back to 3D, by exploiting a collection of aligned 3D models of related objects. Our key insight is that, even when the imaged object is not contained in the shape collection, the network of shapes implicitly characterizes a shape-specific deformation subspace that regularizes the problem and enables robust diffusion of depth information from the shape collection to the input image. We evaluate our fully automatic approach on diverse and challenging input images, validate the results against Kinect depth readings, and demonstrate several imaging applications including depth-enhanced image editing and image relighting.

Original languageEnglish (US)
Article number37
JournalACM Transactions on Graphics
Volume33
Issue number4
DOIs
StatePublished - Jan 1 2014
Event41st International Conference and Exhibition on Computer Graphics and Interactive Techniques, ACM SIGGRAPH 2014 - Vancouver, BC, Canada
Duration: Aug 10 2014Aug 14 2014

Keywords

  • Data-driven shape analysis
  • Depth estimation
  • Image retrieval
  • Pose estimation
  • Shape collections

ASJC Scopus subject areas

  • Computer Graphics and Computer-Aided Design

Fingerprint Dive into the research topics of 'Estimating image depth using shape collections'. Together they form a unique fingerprint.

Cite this