A tracking and registration method based on ORB and KLT for augmented reality system

Xia, Ying; Zhou, Wenjing

doi:10.1109/wocc.2013.6676390

Cited by 3 publications

(1 citation statement)

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“…For feature point matching such as SIFT, these encoded feature points are robust against changes in image scale or even partially occlusion. The review of recent methods can be found in [6], [7], [8]. The feature point extraction relies on searching for potential local extrema and the generation of SIFT descriptor for the subsequent feature matching.…”

Section: Related Workmentioning

confidence: 99%

Content Authoring Using Single Image in Urban Environments for Augmented Reality

Cho¹,

et al. 2016

2016 International Conference on Digital Image Computing: Techniques and Applications (DICTA)

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Content authoring is one of essentials of Augmented Reality (AR), which is to emplace an augmented content on a true part of a real scene in order to enhance users' visual experience. For the case of street view single 2D images, the challenge emerges because of clutter environments and unknown position and orientation related to camera pose. Although existing methods based on 2D feature point matching or vanishing point registration may recover the camera pose, the robustness is always challenging because of the uncertainty of feature point detection on texture-less region and displacement of vanishing point detection caused by irregular lines detected on the scene. By taking the advantages of characteristics of the man-made object (e.g. building) widely seen on the street view, this paper proposes a simple yet efficient content authoring approach. In this approach, the building dominant plane where the virtual object will be emplaced is detected and then projected to the frontal-parallel view on which the virtual object can be reliably emplaced. Once the virtual object and the true scene are embedded to each other on the frontal-parallel view, they are able to be converted back to the original view using inverse projection without any distortion. Experiments on public databases show that the proposed method can recover camera pose and implement content emplacement with promising performance.

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Section: Related Workmentioning

confidence: 99%