Efficient Graph-Based Image Segmentation
International Journal of Computer Vision
Modelling and Interpretation of Architecture from Several Images
International Journal of Computer Vision
ACM SIGGRAPH 2005 Papers
ARVino — Outdoor Augmented Reality Visualisation of Viticulture GIS Data
ISMAR '05 Proceedings of the 4th IEEE/ACM International Symposium on Mixed and Augmented Reality
Interactive 3D architectural modeling from unordered photo collections
ACM SIGGRAPH Asia 2008 papers
Comprehensible Visualization for Augmented Reality
IEEE Transactions on Visualization and Computer Graphics
A sketch-based interface for photo pop-up
Proceedings of the 6th Eurographics Symposium on Sketch-Based Interfaces and Modeling
Fast annotation and modeling with a single-point laser range finder
ISMAR '08 Proceedings of the 7th IEEE/ACM International Symposium on Mixed and Augmented Reality
Importance masks for revealing occluded objects in augmented reality
Proceedings of the 16th ACM Symposium on Virtual Reality Software and Technology
Technical Section: Annotation in outdoor augmented reality
Computers and Graphics
Interactive multi-label segmentation
ACCV'10 Proceedings of the 10th Asian conference on Computer vision - Volume Part I
HYDROSYS: a mixed reality platform for on-site visualization of environmental data
W2GIS'11 Proceedings of the 10th international conference on Web and wireless geographical information systems
Online Creation of Panoramic Augmented Reality Annotations on Mobile Phones
IEEE Pervasive Computing
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A convincing combination of virtual and real data in an Augmented Reality (AR) application requires detailed 3D information about the real world scene. In many situations extensive model data is not available, while sparse representations such as outlines on a map exist. In this paper, we present a novel approach using such sparse 3D model data to seed automatic image segmentation and infer a dense depth map of an environment. Sparse 3D models of known landmarks, such as points and lines from GIS databases, are projected into a registered image and initialize 2D image segmentation at the projected locations in the image. For the segmentation we propose different techniques, which combine shape information, semantics given by the database, and the visual appearance in the referenced image. The resulting depth information of objects in the scene can be used in many applications, including occlusion handling, label placement, and 3D modeling.