3D Organization of 2D Urban Imagery

Cho, P.

doi:10.1109/aipr.2007.12

Cited by 10 publications

(4 citation statements)

References 4 publications

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“…Highlighted in Cho (2007) is the ability to define accurately a camera's location based on the calculation of its viewing frustum. This work constructs 3D imagery from 2D camera pictures without the spatial location of the camera being known.…”

Section: Academic Applicationsmentioning

confidence: 99%

Spatial video and GIS

Lewis

Fotheringham

Winstanley

2011

International Journal of Geographical Information Science

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Section: Academic Applicationsmentioning

confidence: 99%

Spatial video and GIS

Lewis

Fotheringham

Winstanley

2011

International Journal of Geographical Information Science

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“…The 2D photo captures panchromatic reflectivities which the 3D ladar image lacks. So to maximize information content, we fuse the two together using an HSV coloring scheme [17]. The fused result is displayed on an absolute longitude-latitude grid in fig.…”

Section: Georegistering Mosaicsmentioning

confidence: 99%

Image-based querying of urban photos and videos

Cho

Bae²,

Durand³

2009

2009 IEEE Applied Imagery Pattern Recognition Workshop (AIPR 2009)

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We extend recent automated computer vision algorithms to reconstruct the global three-dimensional structures for photos and videos shot at fixed points in outdoor city environments. Mosaics of digital stills and embedded videos are georegistered by matching a few of their 2D features with 3D counterparts in aerial ladar imagery. Once image planes are aligned with world maps, abstract urban knowledge can propagate from the latter into the former. We project geotagged annotations from a 3D map into a 2D video stream and demonstrate their tracking buildings and streets in a clip with significant panning motion. We also present an interactive tool which enables users to select city features of interest in video frames and retrieve their geocoordinates and ranges. Implications of this work for future augmented reality systems based upon mobile smart phones are discussed.

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“…For example, we can identify the buildings in an image corresponding to a specific restaurant if the information is given on maps. Note that while extracting builds from images has been addressed through a similar strategy of projecting georeferenced models [6,17], those studies use existing building models instead of 2D maps.…”

Section: Introductionmentioning

confidence: 99%

Combining maps and street level images for building height and facade estimation

Yuan

Cheriyadat

2016

Proceedings of the 2nd ACM SIGSPATIAL Workshop on Smart Cities and Urban Analytics

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We propose a method that integrates two widely available data sources, building footprints from 2D maps and street level images, to derive valuable information that is generally difficult to acquire -building heights and building facade masks in images. Building footprints are elevated in world coordinates and projected onto images. Building heights are estimated by scoring projected footprints based on their alignment with building features in images. Building footprints with estimated heights can be converted to simple 3D building models, which are projected back to images to identify buildings. In this procedure, accurate camera projections are critical. However, camera position errors inherited from external sensors commonly exist, which adversely affect results. We derive a solution to precisely locate cameras on maps using correspondence between image features and building footprints. Experiments on real-world datasets show the promise of our method.

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3D Organization of 2D Urban Imagery

Cited by 10 publications

References 4 publications

Spatial video and GIS

Spatial video and GIS

Image-based querying of urban photos and videos

Combining maps and street level images for building height and facade estimation

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