Matching buildings: pose estimation in an urban environment

Coors, Volker; Huch, T.; Kretschmer, Ursula

doi:10.1109/isar.2000.880928

Cited by 18 publications

(6 citation statements)

References 4 publications

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“…The drive to build applications without relying on artificial markers or fiducials has placed tremendous focus on natural feature recognition and tracking in the AR community [4,5,7,8,12,13,14,17,18,19,21].…”

Section: Related Workmentioning

confidence: 99%

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Applying robust structure from motion to markerless augmented reality

Mooser

You

Neumann

et al. 2009

2009 Workshop on Applications of Computer Vision (WACV)

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We demonstrate a complete system for markerless augmented reality using robust structure from motion. The proposed system includes two main components. The first is a means of learning the appearance of complex 3D objects and augmenting them with virtual annotations. Its output is database of recognizable landmarks along with 3D descriptions of accompanying virtual objects. The second component uses this data to recognize the previously learned landmarks, recover camera pose, and render the associated virtual content.Both components make use of the recently developed subtrack optimization algorithm for structure from motion, which we demonstrate to be a useful tool for both learning the structure of objects and tracking camera pose after recognition. The complete system is demonstrated on several complex real-world examples.

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Section: Related Workmentioning

confidence: 99%

“…Proposed solutions often target a specific class of objects such as urban buildings [4] or planar objects [5,17]. Others make use of specialized hardware such as calibrated multicamera rigs [15,21].…”

Section: Related Workmentioning

confidence: 99%

Applying robust structure from motion to markerless augmented reality

Mooser

You

Neumann

et al. 2009

2009 Workshop on Applications of Computer Vision (WACV)

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“…Tracked 2D features may also be used as a basis for camera pose estimation [14]. Other trackers target on specific classes of features [1] such as buildings silhouettes [10] or planar structures [26].…”

Section: Related Workmentioning

confidence: 99%

Real-Time Object Tracking for Augmented Reality Combining Graph Cuts and Optical Flow

Mooser

You

Neumann

2007

2007 6th IEEE and ACM International Symposium on Mixed and Augmented Reality

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Figure 1: Tracking and annotating an object using graph cut segmentation. A building sign on the USC campus is first detected using simple recognition, after which no additional information is needed. As the camera moves, we segment and track the sign through significant scale and orientation changes, rendering an annotation above it. This example illustrates a possible navigation tool for exploring buildings on campus. In this case, the annotation tells the user that the building is Powell Hall, and that the offices inside include the CGIT Lab, the IMSC offices, and the EE department. ABSTRACTWe present an efficient and accurate object tracking algorithm based on the concept of graph cut segmentation. The ability to track visible objects in real-time provides an invaluable tool for the implementation of markerless Augmented Reality. Once an object has been detected, it's location in future frames can be used to position virtual content, and thus annotate the environment.Unlike many object tracking algorithms, our approach does not rely on a preexisting 3D model or any other information about the object or its environment. It takes, as input, a set of pixels representing an object in an initial frame and uses a combination of optical flow and graph cut segmentation to determine the corresponding pixels in each future frame. Experiments show that our algorithm robustly tracks objects of disparate shapes and sizes over hundreds of frames, and can even handle difficult cases where an object contains many of the same colors as its background. We further show how this technology can be applied to practical AR applications.

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“…A system for the alignment of terrestrial images to a 3D city model based on an approximate exterior orientation from GPS and digital compass has been discussed by Jaynes and Partington (1999). A similar approach, which is also based on the matching of linear features of the building facades, is described by Coors et al (2000). In their approach a very detailed model of the building facade, including features like windows, doors or arches, is required.…”

Section: Image Georeferencing In Urban Environmentsmentioning

confidence: 99%

A multi-sensor system for positioning in urban environments

Haala

Böhm

2003

ISPRS Journal of Photogrammetry and Remote Sensing

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Within the article a low-cost system for the provision of georeferenced terrestrial images in urban environments is presented. Based on an image with approximate exterior orientation from a low-cost GPS and a digital compass and a 3D CAD model of a visible model as provided from a 3D virtual city model, the exact location of the building in the image is detected automatically and used for a refined orientation of the image. The work is part of a project aiming on the development of a mobile device, which enables access to location-based services in a complex urban environment. The intuitive access to object-related information is realized by so-called telepointing. For this purpose, a spatial model of the user's environment is mapped to an oriented image, allowing for the access to object-related information by pointing to the respective image sections. Since the provision of location-based services currently is one of the most promising markets for the use of spatial data in urban areas, these applications will also be discussed. D

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Matching buildings: pose estimation in an urban environment

Cited by 18 publications

References 4 publications

Applying robust structure from motion to markerless augmented reality

Applying robust structure from motion to markerless augmented reality

Real-Time Object Tracking for Augmented Reality Combining Graph Cuts and Optical Flow

A multi-sensor system for positioning in urban environments

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