From Google Street View to 3D city models

Torii, Akihiko; Havlena, Michal; Pajdla, Tomáš

doi:10.1109/iccvw.2009.5457551

Cited by 78 publications

(63 citation statements)

References 17 publications

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“…Our problem is different since we follow a long trajectory in wich we have only a few panoramas sharing the same view of the scene. This aspect of the problem was also studied in [9,15] with different techniques.…”

Section: Multiple Images Matchingmentioning

confidence: 99%

“…We also present, and this is our main contribution (refer to Subsection 2.4), a matching scheme that augments the matching result by applying the scheme on the specific panorama where the classical methods fail to find a sufficient number of matches. The proposed matching scheme does not replace the classical matching scheme used in large image set such as the one described in [13] or [15], it is rather a supplementary step in the matching phase.…”

Section: Multiple Images Matchingmentioning

confidence: 99%

“…This is also the case with planar images and this is one of the reasons why ASIFT [10] was introduced by Morel and Yu. The methods presented in [3,4,6,15] do not take this fact into account. Mauthner et al [9] uses local affine frame to compensate for perspective changes.…”

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

“…It was also studied in the case of images with radial distortion [6], wide angle images [4] and spherical panoramas [15]. The strategy used in [3,9,15] is to work with reprojections of the images to be matched. As for [4,6], they rather modify the convolution step in the definition of the SIFT features so that it applies on wide angles images [4] or on images with radial distortion [6].…”

Section: Introductionmentioning

confidence: 99%

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Matching cylindrical panorama sequences using planar reprojections

Carufel

Laganière

2011

2011 IEEE International Conference on Computer Vision Workshops (ICCV Workshops)

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This paper presents a matching scheme for large set of omnidirectional images sequentially captured in an urban environment. Most classical image matching methods when applied to cylindrical panoramas taken in large environments does not always produce a sufficient number of matches. In this work, our objective is to making sure that the full set of panoramas remains as connected as possible at all geographical locations even if only a few panoramas sharing the same view of the scene are available. For this matter, we present a matching strategy that augments the accuracy and the number of match points in the context of urban panorama matching. To improve matching results, the method simulates different local transformations at chosen view directions of the panoramas. We show that our matching scheme improves the matching result on the specific panoramas where the classical methods fail to find a sufficient number of matches. This conclusion is supported by real-world experiments performed on 8017 pairs of images coming from 763 different images.

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Section: Multiple Images Matchingmentioning

confidence: 99%

Section: Multiple Images Matchingmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Matching cylindrical panorama sequences using planar reprojections

Carufel

Laganière

2011

2011 IEEE International Conference on Computer Vision Workshops (ICCV Workshops)

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City‐scale urban sustainability: Spatiotemporal mapping of distributed solar power for New York City

Taminiau¹,

Byrne

2020

WIREs Energy & Environment

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Research into urban sustainability increasingly deploys advanced analytics and large-scale data to investigate possible sustainable energy options. This paper reviews several available data-driven approaches that answer urban sustainability questions. One such approach is applied to identify energy consumption and rooftop photovoltaic (PV) generation potential in New York City at the electricity network level with the objective of improving the city's resilience to expected impacts of climate change. Electric system resilience is, in part, dependent on the spatial and temporal distribution of consumption and potential sustainable energy generation which is investigated here by separating the city into 68 electricity networks and evaluating their generationconsumption interaction pattern. The analysis reveals that New York City could be home to about 10 GWp of rooftop solar installations-sufficient to cover approximately 25% of annual city electricity consumption and 53% of daylight hour consumption. Localized electricity import-export dimensions are explored for each of the 68 electricity networks and we identify an excess 3.1 TWh electricity supply per year if the entire technical potential of rooftop solar PV is deployed. This excess electricity supply is roughly equivalent to an annual $734 million value which could benefit low-income areas in the city.

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Using Google Street View for Street-Level Urban Form Analysis, a Case Study in Cambridge, Massachusetts

Ratti

2019

The Mathematics of Urban Morphology

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From Google Street View to 3D city models

Cited by 78 publications

References 17 publications

Matching cylindrical panorama sequences using planar reprojections

Matching cylindrical panorama sequences using planar reprojections

City‐scale urban sustainability: Spatiotemporal mapping of distributed solar power for New York City

Using Google Street View for Street-Level Urban Form Analysis, a Case Study in Cambridge, Massachusetts

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