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Automated image-based 3D reconstruction methods are more and more flooding our 3D modeling applications. Fully automated solutions give the impression that from a sample of randomly acquired images we can derive quite impressive visual 3D models. Although the level of automation is reaching very high standards, image quality is a fundamental pre-requisite to produce successful and photo-realistic 3D products, in particular when dealing with large datasets of images. This article presents an efficient pipeline based on color enhancement, image denoising, color-to-gray conversion and image content enrichment. The pipeline stems from an analysis of various state-of-the-art algorithms and aims to adjust the most promising methods, giving solutions to typical failure causes. The assessment evaluation proves how an effective image pre-processing, which considers the entire image dataset, can improve the automated orientation procedure and dense 3D point cloud reconstruction, even in the case of poor texture scenarios.

For more than thirty years, 3D digital modelling has been used more and more widely as a research tool in various disciplinary fields. Despite this, the 3D models produced by different research, investigation, and speculation activities are still only used as a basis and as sources for the production of images and scientific contributions (papers in journals, contributions in conference proceedings, etc.) in dissemination and cultural activities, but without having yet assumed full autonomy as a ‘scientific fact’, as a product of research, or as a means of scientific debate and progress. This paper outlines the context in the field of architecture and archeology in which the use of 3D models has become increasingly widespread, reaching a level of full maturity, and how the field of hypothetical reconstruction can be characterized as an autonomous/scientific discipline through the definition and adoption of a scientific, transparent, verifiable, reusable, and refutable method. In this context, the definition of the 3D model as a product of scientific speculation and research is proposed.

Ensuring color fidelity in image-based 3D modeling of heritage scenarios is nowadays still an open research matter. Image colors are important during the data processing as they affect algorithm outcomes, therefore their correct treatment, reduction and enhancement is fundamental. In this contribution, we present an automated solution developed to improve the radiometric quality of an image datasets and the performances of two main steps of the photogrammetric pipeline (camera orientation and dense image matching). The suggested solution aims to achieve a robust automatic color balance and exposure equalization, stability of the RGB-to-gray image conversion and faithful color appearance of a digitized artifact. The innovative aspects of the article are: complete automation, better color target detection, a MATLAB implementation of the ACR scripts created by Fraser and the use of a specific weighted polynomial regression. A series of tests are presented to demonstrate the efficiency of the developed methodology and to evaluate color accuracy (‘color characterization’).

Nowadays digital replicas of artefacts belonging to the Cultural Heritage (CH) are one of the most promising innovations for museums exhibitions, since they foster new forms of interaction with collections, at different scales. However, practical digitization is still a complex task dedicated to specialized operators. Due to these premises, this paper introduces a novel approach to support non-experts working in museums with robust, easy-to-use workflows based on low-cost widespread devices, aimed at the study, classification, preservation, communication and restoration of CH artefacts. The proposed methodology introduces an automated combination of acquisition, based on mobile equipment and visualization, based on Real-Time Rendering. After the description of devices used along the workflow, the paper focuses on image pre-processing and geometry processing techniques adopted to generate accurate 3D models from photographs. Assessment criteria for the developed process evaluation are illustrated. Tests of the methodology on some effective museum case studies are presented and discussed.

A shared commitment to standardising the process of hypothetically reconstructing lost buildings from the past has characterised academic research in recent years and can manifest at various stages of the reconstructive process and with different perspectives. This research specifically aims to establish a user-independent and traceable procedure that can be applied at the end of the reconstructive process to quantify the average level of uncertainty of 3D digital architectural models. The procedure consists of applying a set of mathematical formulas based on numerical values retrievable from a given scale of uncertainty and developed to simplify reuse and improve transparency in reconstructive 3D models. This effort to assess uncertainty in the most user-independent way possible will contribute to producing 3D models that are more comparable to each other and more transparent for academic researchers, professionals, and laypersons who wish to reuse them. Being able to calculate a univocal numerical value that gives information on the global average uncertainty of a certain reconstructive model is an additional synthetic way, together with the more visual false-colour scale of uncertainty, to help disseminate the work in a clear and transmissible way. However, since the hypothetical reconstructive process is a process based on personal interpretation, which inevitably requires a certain level of subjectivity, it is crucial to define a methodology to assess and communicate this subjectivity in a user-independent and reproducible way.

This paper presents preliminary findings from the ongoing Erasmus+ European project CoVHer, which focuses on the hypothetical virtual reconstructions of lost or unbuilt architecture. This contribution provides a critical assessment of the terminology specific to this field. A significant challenge lies in the absence of standardised terminology for many foundational aspects, which can lead to misconceptions and misunderstandings. The research begins with a comprehensive review of the relevant literature in the field, aiming to identify which terms are most widely accepted and appropriate to use and which are ambiguous or should be avoided. For concepts that lack a clear definition, new terminology is proposed. This paper analyses six key terms: reconstruction, uncertainty, raw model, informative model, digital representation methods, and 3D modelling techniques.

For the hypothetical reconstruction of architectural heritage, there are still no scientific standards of reference concerning their sharing and documentation. Recent international initiatives established the basis to address this problem; however, still, much work needs to be done in order to systematise good practices for the process of reconstruction and its dissemination. This contribution aims to take a step forward in the analysis and visualisation of uncertainty. Some authors have suggested various approaches to visualise uncertainty for single buildings; however, case studies at the urban scale are rarely investigated. This research proposes an improved source-based multi-feature approach aimed at analysing and visualising (through false-colour shading) the uncertainty of hypothetical 3D digital models of urban areas. The assessment of uncertainty is also quantified qualitatively by using newly defined formulas which calculate the average uncertainty weighted on the volume of the 3D model. This methodology aims to improve the objectiveness, unambiguity, transparency, reusability, and readability of hypothetical reconstructive 3D models, and its use is exemplified in the case study of the hypothetical reconstruction of Piazza delle Erbe in Verona, a project presented in the form of a docufilm at EXPO 2015 in Milan.

This contribution to the literature presents an in-depth analysis of the scene drawn by Leonardo da Vinci in the preparatory drawing, Adoration of the Magi, dated 1481, that is now housed at the Département des Arts Graphiques du Musée du Louvre in Paris, France. This analysis focuses on the architectural elements and highlights how the drawing discloses three distinct vanishing points/centers of vision: one for the classical architecture to the left of the scene, one for the Nativity hut, and one for the structure with stairs. If we consider the structures as belonging to the same 2D projected space, at least two must be depicted out of square; conversely, if we consider them as straight, standard structures, they cannot belong to the same 2D projected space. Given this assumption, we propose, on the one hand, some variations of the scene where the structures are straightened and projected according to only one of the viewpoints at a time; on the other hand, a set of variants of the scene are presented, considering the out-of-square structures. The scenes are generated by applying inverse perspective projections. These results prompt a discussion on possible reasons why Leonardo made these conscious or unconscious “formal errors”.

Two case studies of hypothetical reconstruction are presented according to the principles of the Critical Digital Model [Apollonio et al. 2019]. One of the case studies is an architecture that was designed but never realized, and the other is the reconstruction of a historical art exhibition hosted into an architecture still existing today. This study is part of a wider research which is being currently carried out in the international Erasmus + project CoVHer. The main objective of the CoVHer project is to identify shared standards for the construction, evaluation and sharing of 3D hypothetical reconstructions. This research exploits the diversities of the two case studies to classify and re-define the methods of digital representation, which deal with the geometrical/mathematical nature of the models themselves and tries to define good practices to produce scientifically valid, sharable, and reusable 3D reconstructions.