Depth based object detection from partial pose estimation of symmetric objects

Ehud Barnea, Ohad Ben-Shahar

Research output: Contribution to journalConference articlepeer-review

6 Scopus citations


Category-level object detection, the task of locating object instances of a given category in images, has been tackled with many algorithms employing standard color images. Less attention has been given to solving it using range and depth data, which has lately become readily available using laser and RGB-D cameras. Exploiting the different nature of the depth modality, we propose a novel shape-based object detector with partial pose estimation for axial or reflection symmetric objects. We estimate this partial pose by detecting target's symmetry, which as a global mid-level feature provides us with a robust frame of reference with which shape features are represented for detection. Results are shown on a particularly challenging depth dataset and exhibit significant improvement compared to the prior art.

Original languageEnglish
Pages (from-to)377-390
Number of pages14
JournalLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume8693 LNCS
Issue numberPART 5
StatePublished - 1 Jan 2014
Event13th European Conference on Computer Vision, ECCV 2014 - Zurich, Switzerland
Duration: 6 Sep 201412 Sep 2014


  • 3D computer vision
  • Object detection
  • Partial pose estimation
  • Range data

ASJC Scopus subject areas

  • Theoretical Computer Science
  • General Computer Science


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