Computer Vision: Three-Dimensional Data from Images
Computer Vision: Three-Dimensional Data from Images
Real-Time Visual Tracking of Complex Structures
IEEE Transactions on Pattern Analysis and Machine Intelligence
Real-Time Localisation and Mapping with Wearable Active Vision
ISMAR '03 Proceedings of the 2nd IEEE/ACM International Symposium on Mixed and Augmented Reality
Distinctive Image Features from Scale-Invariant Keypoints
International Journal of Computer Vision
Stable Real-Time 3D Tracking Using Online and Offline Information
IEEE Transactions on Pattern Analysis and Machine Intelligence
Combining Edge and Texture Information for Real-Time Accurate 3D Camera Tracking
ISMAR '04 Proceedings of the 3rd IEEE/ACM International Symposium on Mixed and Augmented Reality
Scene Modelling, Recognition and Tracking with Invariant Image Features
ISMAR '04 Proceedings of the 3rd IEEE/ACM International Symposium on Mixed and Augmented Reality
Real-Time Markerless Tracking for Augmented Reality: The Virtual Visual Servoing Framework
IEEE Transactions on Visualization and Computer Graphics
CVPR '06 Proceedings of the 2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition - Volume 1
Scalable Recognition with a Vocabulary Tree
CVPR '06 Proceedings of the 2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition - Volume 2
Construction of Feature Landmark Database Using Omnidirectional Videos and GPS Positions
3DIM '07 Proceedings of the Sixth International Conference on 3-D Digital Imaging and Modeling
Online camera pose estimation in partially known and dynamic scenes
ISMAR '06 Proceedings of the 5th IEEE and ACM International Symposium on Mixed and Augmented Reality
Going out: robust model-based tracking for outdoor augmented reality
ISMAR '06 Proceedings of the 5th IEEE and ACM International Symposium on Mixed and Augmented Reality
Semi-Autonomous Generation of Appearance-based Edge Models from Image Sequences
ISMAR '07 Proceedings of the 2007 6th IEEE and ACM International Symposium on Mixed and Augmented Reality
Ninja on a Plane: Automatic Discovery of Physical Planes for Augmented Reality Using Visual SLAM
ISMAR '07 Proceedings of the 2007 6th IEEE and ACM International Symposium on Mixed and Augmented Reality
Parallel Tracking and Mapping for Small AR Workspaces
ISMAR '07 Proceedings of the 2007 6th IEEE and ACM International Symposium on Mixed and Augmented Reality
Wide area localization on mobile phones
ISMAR '09 Proceedings of the 2009 8th IEEE International Symposium on Mixed and Augmented Reality
Estimating camera position and posture by using feature landmark database
SCIA'05 Proceedings of the 14th Scandinavian conference on Image Analysis
Editorial: Special Section on Mobile Augmented Reality
Computers and Graphics
Mobile AR using pre-captured omnidirectional images
SIGGRAPH Asia 2013 Symposium on Mobile Graphics and Interactive Applications
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In the field of augmented reality (AR), many kinds of vision-based extrinsic camera parameter estimation methods have been proposed to achieve geometric registration between real and virtual worlds. Previously, a feature landmark-based camera parameter estimation method was proposed. This is an effective method for implementing outdoor AR applications because a feature landmark database can be automatically constructed using the structure-from-motion (SfM) technique. However, the previous method cannot work in real time because it entails a high computational cost or matching landmarks in a database with image features in an input image. In addition, the accuracy of estimated camera parameters is insufficient for applications that need to overlay CG objects at a position close to the user's viewpoint. This is because it is difficult to compensate for visual pattern change of close landmarks when only the sparse depth information obtained by the SfM is available. In this paper, we achieve fast and accurate feature landmark-based camera parameter estimation by adopting the following approaches. First, the number of matching candidates is reduced to achieve fast camera parameter estimation by tentative camera parameter estimation and by assigning priorities to landmarks. Second, image templates of landmarks are adequately compensated for by considering the local 3-D structure of a landmark using the dense depth information obtained by a laser range sensor. To demonstrate the effectiveness of the proposed method, we developed some AR applications using the proposed method.