WFRF:(Aydemir Alper)
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Exploiting and modeling local 3D structure for predicting object locations
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- Aydemir, Alper (author)
- KTH,Datorseende och robotik, CVAP,Centrum för Autonoma System, CAS
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- Jensfelt, Patric, 1972- (author)
- KTH,Datorseende och robotik, CVAP,Centrum för Autonoma System, CAS
- (creator_code:org_t)
- IEEE, 2012
- 2012
- English.
- In: Intelligent Robots and Systems (IROS), 2012 IEEE/RSJ International Conference on. - : IEEE. - 9781467317375 ; , s. 3885-3892
- Conference paper (peer-reviewed)
Abstract
Subject headings
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- In this paper, we argue that there is a strong correlation between local 3D structure and object placement in everyday scenes. We call this the 3D context of the object. In previous work, this is typically hand-coded and limited to flat horizontal surfaces. In contrast, we propose to use a more general model for 3D context and learn the relationship between 3D context and different object classes. This way, we can capture more complex 3D contexts without implementing specialized routines. We present extensive experiments with both qualitative and quantitative evaluations of our method for different object classes. We show that our method can be used in conjunction with an object detection algorithm to reduce the rate of false positives. Our results support that the 3D structure surrounding objects in everyday scenes is a strong indicator of their placement and that it can give significant improvements in the performance of, for example, an object detection system. For evaluation, we have collected a large dataset of Microsoft Kinect frames from five different locations, which we also make publicly available.
Subject headings
- TEKNIK OCH TEKNOLOGIER -- Elektroteknik och elektronik -- Robotteknik och automation (hsv//swe)
- ENGINEERING AND TECHNOLOGY -- Electrical Engineering, Electronic Engineering, Information Engineering -- Robotics (hsv//eng)
Keyword
- Intelligent systems
- Object recognition
- Three dimensional computer graphics
Publication and Content Type
- ref (subject category)
- kon (subject category)
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