Unfolding Leonardo Da Vinci’s Globe (Ad 1504) to Reveal Its Historical World Map

Verhoeven, Geert; Missinne, Stefaan

doi:10.5194/isprs-annals-iv-2-w2-303-2017

Cited by 6 publications

(5 citation statements)

References 16 publications

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“…There are various other 3D modelling approaches, distinguishable as non-destructive and destructive techniques. As examples for non-destructive techniques for monuments and artworks, multispectral analysis supports mainly material analysis of multi-layered surfaces, e.g., of paintings [172,434], but also for globes [435] or sculptures [171]. IR thermography detects emissions in the infrared thermal range of the electromagnetic spectrum to measure heat emission [436].…”

Section: Othersmentioning

confidence: 99%

Digital 3D Technologies for Humanities Research and Education: An Overview

Münster¹

2022

Applied Sciences

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Digital 3D modelling and visualization technologies have been widely applied to support research in the humanities since the 1980s. Since technological backgrounds, project opportunities, and methodological considerations for application are widely discussed in the literature, one of the next tasks is to validate these techniques within a wider scientific community and establish them in the culture of academic disciplines. This article resulted from a postdoctoral thesis and is intended to provide a comprehensive overview on the use of digital 3D technologies in the humanities with regards to (1) scenarios, user communities, and epistemic challenges; (2) technologies, UX design, and workflows; and (3) framework conditions as legislation, infrastructures, and teaching programs. Although the results are of relevance for 3D modelling in all humanities disciplines, the focus of our studies is on modelling of past architectural and cultural landscape objects via interpretative 3D reconstruction methods.

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Section: Othersmentioning

confidence: 99%

Digital 3D Technologies for Humanities Research and Education: An Overview

Münster¹

2022

Applied Sciences

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“…However, the composite image contains zones of differing magnification and viewpoint making it problematic for photogrammetric self-calibration and the interior and exterior orientation determination. Focus stacking also increases the acquisition and processing times due to the increased number of images, often being cited as reasons that focus stacking was not selected (Gallo et al, 2014;Marziali and Dionisio, 2017;Verhoeven and Missinne, 2017;Sapirstein, 2018).…”

Section: Improved Dof For 3d Imagingmentioning

confidence: 99%

Quantifying Depth of Field and Sharpness for Image-Based 3d Reconstruction of Heritage Objects

Webb

Robson

Evans

2020

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

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Abstract. Image-based 3D reconstruction processing tools assume sharp focus across the entire object being imaged, but depth of field (DOF) can be a limitation when imaging small to medium sized objects resulting in variation in image sharpness with range from the camera. While DOF is well understood in the context of photographic imaging and it is considered with the acquisition for image-based 3D reconstruction, an “acceptable” level of sharpness and associated “circle of confusion” has not yet been quantified for the 3D case. The work described in this paper contributes to the understanding and quantification of acceptable sharpness by providing evidence of the influence of DOF on the 3D reconstruction of small to medium sized museum objects. Spatial frequency analysis using established collections photography imaging guidelines and targets is used to connect input image quality with 3D reconstruction output quality. Combining quantitative spatial frequency analysis with metrics from a series of comparative 3D reconstructions provides insights into the connection between DOF and output model quality. Lab-based quantification of DOF is used to investigate the influence of sharpness on the output 3D reconstruction to better understand the effects of lens aperture, camera to object surface angle, and taking distance. The outcome provides evidence of the role of DOF in image-based 3D reconstruction and it is briefly presented how masks derived from image content and depth maps can be used to remove unsharp image content and optimise structure from motion (SfM) and multiview stereo (MVS) workflows.

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“…Image collection two holds four images of an ostrich egg globe attributed to Leonardo da Vinci. These images, which were acquired to digitally unfold the globe into a projected map (Verhoeven and Missinne, 2017), were also shot with a Nikon D750. In this case, a prime 105 mm lens was used, and f/22 dialled for all exposures.…”

Section: Globus Of Leonardo Da Vincimentioning

confidence: 99%

Focusing on Out-of-Focus: Assessing Defocus Estimation Algorithms for the Benefit of Automated Image Masking

Verhoeven

2018

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

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Acquiring photographs as input for an image-based modelling pipeline is less trivial than often assumed. Photographs should be correctly exposed, cover the subject sufficiently from all possible angles, have the required spatial resolution, be devoid of any motion blur, exhibit accurate focus and feature an adequate depth of field. The last four characteristics all determine the “sharpness” of an image and the photogrammetric, computer vision and hybrid photogrammetric computer vision communities all assume that the object to be modelled is depicted “acceptably” sharp throughout the whole image collection. Although none of these three fields has ever properly quantified “acceptably sharp”, it is more or less standard practice to mask those image portions that appear to be unsharp due to the limited depth of field around the plane of focus (whether this means blurry object parts or completely out-of-focus backgrounds). This paper will assess how well- or ill-suited defocus estimating algorithms are for automatically masking a series of photographs, since this could speed up modelling pipelines with many hundreds or thousands of photographs. To that end, the paper uses five different real-world datasets and compares the output of three state-of-the-art edge-based defocus estimators. Afterwards, critical comments and plans for the future finalise this paper.

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Unfolding Leonardo Da Vinci’s Globe (Ad 1504) to Reveal Its Historical World Map

Cited by 6 publications

References 16 publications

Digital 3D Technologies for Humanities Research and Education: An Overview

Digital 3D Technologies for Humanities Research and Education: An Overview

Quantifying Depth of Field and Sharpness for Image-Based 3d Reconstruction of Heritage Objects

Focusing on Out-of-Focus: Assessing Defocus Estimation Algorithms for the Benefit of Automated Image Masking

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