Targetless Camera Calibration

Barazzetti, Luigi; Mussio, L.; Remondino, Fabio; Scaioni, Marco

doi:10.5194/isprsarchives-xxxviii-5-w16-335-2011

Cited by 39 publications

(20 citation statements)

References 26 publications

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“…the automated and simultaneous determination of camera parameters together with scene's geometry. SfM has been adopted also in the photogrammetric community (Barazzetti et al, 2010;Del Pizzo & Troisi, 2011;Deseilligny & Clery, 2011;Roncella et al, 2011) although camera calibration and image orientation are normally kept separate unless the image network is acceptable for self-calibration (Barazzetti et al, 2011). In all automated approaches, image correspondences are normally extracted using feature-based methods and then the unknown camera parameters and 3D object coordinates are determined using a bundle adjustment method.…”

Section: Introductionmentioning

confidence: 99%

Evaluation of feature-based methods for automated network orientation

Apollonio

Ballabeni

Gaiani

et al. 2014

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

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Every day new tools and algorithms for automated image processing and 3D reconstruction purposes become available, giving the possibility to process large networks of unoriented and markerless images, delivering sparse 3D point clouds at reasonable processing time. In this paper we evaluate some feature-based methods used to automatically extract the tie points necessary for calibration and orientation procedures, in order to better understand their performances for 3D reconstruction purposes. The performed tests -based on the analysis of the SIFT algorithm and its most used variants -processed some datasets and analysed various interesting parameters and outcomes (e.g. number of oriented cameras, average rays per 3D points, average intersection angles per 3D points, theoretical precision of the computed 3D object coordinates, etc.).

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Section: Introductionmentioning

confidence: 99%

Evaluation of feature-based methods for automated network orientation

Apollonio

Ballabeni

Gaiani

et al. 2014

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

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“…This investigation showed no significant variation in the results obtained, either in IO or EO determination. Barazzetti et al (2011b) also performed a similar investigation for a targetless camera calibration procedure and reported a similarly successful outcome. …”

Section: Experimental Evaluationmentioning

confidence: 85%

Fully Automated Image Orientation in the Absence of Targets

Stamatopoulos

Chuang

Fraser

et al. 2012

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

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ABSTRACT:Automated close-range photogrammetric network orientation has traditionally been associated with the use of coded targets in the object space to allow for an initial relative orientation (RO) and subsequent spatial resection of the images. Over the past decade, automated orientation via feature-based matching (FBM) techniques has attracted renewed research attention in both the photogrammetry and computer vision (CV) communities. This is largely due to advances made towards the goal of automated relative orientation of multi-image networks covering untargetted (markerless) objects. There are now a number of CV-based algorithms, with accompanying open-source software, that can achieve multi-image orientation within narrow-baseline networks. From a photogrammetric standpoint, the results are typically disappointing as the metric integrity of the resulting models is generally poor, or even unknown, while the number of outliers within the image matching and triangulation is large, and generally too large to allow relative orientation (RO) via the commonly used coplanarity equations. On the other hand, there are few examples within the photogrammetric research field of automated markerless camera calibration to metric tolerances, and these too are restricted to narrow-baseline, low-convergence imaging geometry. The objective addressed in this paper is markerless automatic multi-image orientation, maintaining metric integrity, within networks that incorporate wide-baseline imagery. By wide-baseline we imply convergent multi-image configurations with convergence angles of up to around 90o . An associated aim is provision of a fast, fully automated process, which can be performed without user intervention. For this purpose, various algorithms require optimisation to allow parallel processing utilising multiple PC cores and graphics processing units (GPUs).

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“…1a). This subset is needed only in the case the camera is not precalibrated, and camera calibration has to be computed within the BBA for estimating EO, see Barazzetti et al (2011). As an empirical rule, in blocks made up of a number of photos smaller than Nim=30, four rolled photos should be minimally included; when Nim>30, one rolled photos every 8-10 acquired photos should be taken; and 4.…”

Section: Terrestrial Blocksmentioning

confidence: 99%

Technical Aspects Related to the Application of SFM Photogrammetry in High Mountain

Scaioni¹,

Crippa²,

Corti³

et al. 2018

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

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ABSTRACT:Structure-from-Motion (SfM) photogrammetry is a flexible and powerful tool to provide 3D point clouds describing the surface of objects. Due to the easy transportability and low-cost of necessary equipment with respect to laser scanning techniques, SfM photogrammetry has great potential to be applied in harsh high-mountain environment. Here point clouds and derived by-products (DEM's, orthoimages, Virtual-Reality models) are needed to document surface morphology and to investigate dynamic processes such as landslides, avalanches, river and soil erosion, glacier retreat. On the other hand, from both the literature and the direct experience of the authors, there are some technical issues that still deserve thorough investigations. The paper would like to address some open problems and suggest solutions, in particular on regards of the photogrammetric network design, the strategy for georeferencing the final products, and for their comparison within time. The discussion is documented with some examples, mainly from surveying campaigns at the Forni Glacier in Italian Alps.

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Targetless Camera Calibration

Cited by 39 publications

References 26 publications

Evaluation of feature-based methods for automated network orientation

Evaluation of feature-based methods for automated network orientation

Fully Automated Image Orientation in the Absence of Targets

Technical Aspects Related to the Application of SFM Photogrammetry in High Mountain

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