Structure-and-motion pipeline on a hierarchical cluster tree

Farenzena, Michela; Fusiello, Andrea; Gherardi, Riccardo

doi:10.1109/iccvw.2009.5457435

Cited by 104 publications

(59 citation statements)

References 24 publications

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“…There are also scenarios that are in-between these two, for example, the datasets from the Samantha project [6] show density between 10% and 17%, as they cover extended outside areas. …”

Section: Sparse Secondary Structurementioning

confidence: 99%

Sparse Sparse Bundle Adjustment

Konolige¹

2010

Procedings of the British Machine Vision Conference 2010

128

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Sparse Bundle Adjustment (SBA) is a method for simultaneously optimizing a set of camera poses and visible points. It exploits the sparse primary structure of the problem, where connections exist just between points and cameras. In this paper, we implement an efficient version of SBA for systems where the secondary structure (relations among cameras) is also sparse. The method, which we call Sparse SBA (sSBA), integrates an efficient method for setting up the linear subproblem with recent advances in direct sparse Cholesky solvers. sSBA outperforms the current SBA standard implementation on datasets with sparse secondary structure by at least an order of magnitude, while also being more efficient on dense datasets.

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“…There are also scenarios that are in-between these two, for example, the datasets from the Samantha project [6] show density between 10% and 17%, as they cover extended outside areas. …”

Section: Sparse Secondary Structurementioning

confidence: 99%

Sparse Sparse Bundle Adjustment

Konolige¹

2010

Procedings of the British Machine Vision Conference 2010

128

View full text Add to dashboard Cite

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“…Table 1 and Table 2 summarize the results before adjustment, while Table 3 and Table 4 Table 4). (Farenzena et al, 2009). The largest of five resulting models is displayed (287 of 329 images).…”

Section: Resultsmentioning

confidence: 99%

“…Work on hierarchical reconstruction is given in (Nister, 2000;Farenzena et al, 2009;Gherardi et al, 2010;Ni & Dellaert, 2012;Havlena et al 2009, Chen et al, 2016, to name a few. Approaches differ in how the tree structure is defined and in strategies for balancing the tree, i.e.…”

Section: Related Workmentioning

confidence: 99%

Hierarchical Structure From Motion Combining Global Image Orientation and Structureless Bundle Adjustment

Cefalu

Haala

Fritsch

2017

Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci.

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ABSTRACT:Global image orientation techniques aim at estimating camera rotations and positions for a whole set of images simultaneously. One of the main arguments for these procedures is an improved robustness against drifting of camera stations in comparison to more classical sequential approaches. Usually, the process consists of computation of absolute rotations and, in a second step, absolute positions for the cameras. Either the first or both steps rely on the network of transformations arising from relative orientations between cameras. Therefore, the quality of the obtained absolute results is influenced by tensions in the network. These may e.g. be induced by insufficient knowledge of the intrinsic camera parameters. Another reason can be found in local weaknesses of image connectivity. We apply a hierarchical approach with intermediate bundle adjustment to reduce these effects. We adopt efficient global techniques which register image triplets based on fixed absolute camera rotations and scaled relative camera translations but do not involve scene structure elements in the fusion step. Our variant employs submodels of arbitrary size, orientation and scale, by computing relative rotations and scales between -and subsequently absolute rotations and scales for -submodels and is applied hierarchically. Furthermore we substitute classical bundle adjustment by a structureless approach based on epipolar geometry and augmented with a scale consistency constraint.

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“…The images, which can be found at http://www.diegm.uniud.it/fusiello/demo/samantha/, have been featured in a few publications that introduced a hierarchical SfM scheme (Farenzena et al, 2009;Gherardi et al, 2010). This new SfM pipeline was developed to better cope with drift problems in such large datasets.…”

Section: Set 7 -Piazza Bra (Italy)mentioning

confidence: 99%

Mind Your Grey Tones – Examining the Influence of Decolourization Methods on Interest Point Extraction and Matching for Architectural Image-Based Modelling

Verhoeven

Karel

Štuhec

et al. 2015

Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci.

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ABSTRACT:This paper investigates the use of different greyscale conversion algorithms to decolourize colour images as input for two Structurefrom-Motion (SfM) software packages. Although SfM software commonly works with a wide variety of frame imagery (old and new, colour and greyscale, airborne and terrestrial, large-and small scale), most programs internally convert the source imagery to singleband, greyscale images. This conversion is often assumed to have little, if any, impact on the final outcome. To verify this assumption, this article compares the output of an academic and a commercial SfM software package using seven different collections of architectural images. Besides the conventional 8-bit true-colour JPEG images with embedded sRGB colour profiles, for each of those datasets, 57 greyscale variants were computed with different colour-to-greyscale algorithms. The success rate of specific colour conversion approaches can therefore be compared with the commonly implemented colour-to-greyscale algorithms (luma Y'601, luma Y'709, or luminance CIE Y), both in terms of the applied feature extractor as well as of the specific image content (as exemplified by the two different feature descriptors and the various image collections, respectively). Although the differences can be small, the results clearly indicate that certain colour-to-greyscale conversion algorithms in an SfMworkflow constantly perform better than others. Overall, one of the best performing decolourization algorithms turns out to be a newly developed one.

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Structure-and-motion pipeline on a hierarchical cluster tree

Cited by 104 publications

References 24 publications

Sparse Sparse Bundle Adjustment

Sparse Sparse Bundle Adjustment

Hierarchical Structure From Motion Combining Global Image Orientation and Structureless Bundle Adjustment

Mind Your Grey Tones – Examining the Influence of Decolourization Methods on Interest Point Extraction and Matching for Architectural Image-Based Modelling

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