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.

The paper presents some experiments carried out as part of the virtual reconstruction of buildings just documented by partial sketches, or partially built, or no more existing, with the aim (a) to emphasize the use of a semantic construction of the digital model, not only as a means to modeling a building but as a cognitive system, (b) to show conceptual similarity between the treaties and BIM, (c) to propose new and more robust solutions to the 3D modeling from 2D drawings for CH artifacts, able to allow the verification of the assumptions used during the reconstruction pipeline, (d) to make use of interactive technical reference, typically real-time photorealistic rendering, for the visualization of three-dimensional model and of variants snapshots, managed by an iconic for illustrating the method of comparison and guided reading of model’s characters of the steps taken.

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.