Depth-Assisted Rectification of Patches Using RGB-D Consumer Devices to Improve Real-Time Keypoint Matching

João Paulo Lima 1 Francisco Simoes 1 Hideaki Uchiyama 2 Veronica Teichrieb 1 Eric Marchand 2
2 Lagadic - Visual servoing in robotics, computer vision, and augmented reality
CRISAM - Inria Sophia Antipolis - Méditerranée , Inria Rennes – Bretagne Atlantique , IRISA-D5 - SIGNAUX ET IMAGES NUMÉRIQUES, ROBOTIQUE
Abstract : This paper presents a method named Depth-Assisted Rectification of Patches (DARP), which exploits depth information available in RGB-D consumer devices to improve keypoint matching of perspectively distorted images. This is achieved by generating a projective rectification of a patch around the keypoint, which is normalized with respect to perspective distortions and scale. The DARP method runs in real-time and can be used with any local feature detector and descriptor. Evaluations with planar and non-planar scenes show that DARP can obtain better results than existing keypoint matching approaches in oblique poses.
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Communication dans un congrès
Int. Conf. on Computer Vision Theory and Applications, Visapp 2013, Feb 2013, Barcelona, Spain. pp.651-656, 2013
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João Paulo Lima, Francisco Simoes, Hideaki Uchiyama, Veronica Teichrieb, Eric Marchand. Depth-Assisted Rectification of Patches Using RGB-D Consumer Devices to Improve Real-Time Keypoint Matching. Int. Conf. on Computer Vision Theory and Applications, Visapp 2013, Feb 2013, Barcelona, Spain. pp.651-656, 2013. 〈hal-00829930〉

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