Abstract
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 language | English |
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Pages (from-to) | 377-390 |
Number of pages | 14 |
Journal | Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) |
Volume | 8693 LNCS |
Issue number | PART 5 |
DOIs | |
State | Published - 1 Jan 2014 |
Event | 13th European Conference on Computer Vision, ECCV 2014 - Zurich, Switzerland Duration: 6 Sep 2014 → 12 Sep 2014 |
Keywords
- 3D computer vision
- Object detection
- Partial pose estimation
- Range data
ASJC Scopus subject areas
- Theoretical Computer Science
- General Computer Science