Abstract. The 3D reconstruction of cultural heritage artefacts plays a crucial role in documentation, conservation and dissemination. While recent advances in photogrammetry, laser scanning and neural rendering techniques have significantly improved the geometric accuracy and visual realism of digitised assets, the reconstruction of transparent and reflective materials - typical in museal collections - remains a major challenge. Materials such as glass, glazes and varnishes exhibit complex optical behaviours, leading to incomplete or inaccurate 3D models. Recent developments in Gaussian Splatting (GS) offer a potential alternative by enabling efficient, high-fidelity scene representation without explicit surface modelling. However, their application to non-Lambertian and transparent heritage objects remains largely unexplored. This paper presents a study on GS methods for the 3D digitisation of transparent cultural heritage artefacts. Through a series of experimental reconstructions, the work investigates the potential and limitations of GS, highlight the opportunities of hybrid pipelines for addressing long-standing challenges in the digitisation of non-collaborative materials.
M. Medici, Andrea Sterpin, Stefano Settimo et al.· The International Archives o...· 0 citations
Abstract. This paper introduces the 3D-4CH project and its open framework, i.e. a sustainable ecosystem of tools designed to overcome the fragmentation and limited maintainability of previous EU-funded 3D heritage initiatives. Aligned with the European Collaborative Cloud for Cultural Heritage (ECCCH), the framework integrates an end-to-end pipeline for 3D data generation and processing, semantic enrichment and long-term dissemination, including metadata and paradata inclusion. The 3D-4CH initiative bridges the gap between ICT research and operational heritage practices, ensuring the scalability and reproducibility of 3D digital assets for crossinstitutional data sharing and preservation. All software components, including GitHub repositories and online processing frameworks, are openly available, in accordance with open science principles and FAIR data practices. Further information is available at https://www.3d4ch-competencecentre.eu/en/tools/.
F. Arnaoutoglou, P. Bonsma, E. M. Farella et al.· The International Archives o...· 0 citations
Abstract. Among the image-based methods, photogrammetry is a consolidated 3D reconstruction technique able to provide highly accurate metric products, widely exploited in many domains. Photogrammetry is, however, conditioned by the characteristics of the captured scene, with good performance in well-textured areas and limits when non-collaborative surfaces, such as reflective or transparent, are present. In such cases, the photogrammetric reconstruction is often affected by noise, incomplete geometry and artifacts, reducing its final reconstruction quality. In recent years, different AI-based reconstruction methods have emerged as alternative (or complementary) 3D reconstruction and rendering solutions. In particular, 3D Gaussian Splatting (GS) has demonstrated impressive capabilities in rendering photorealistic scenes in challenging situations with high visual fidelity. However, its application in large-scale scenarios or when highly accurate 3D metric products are required is still limited, due to the high computational resources needed and the intrinsic optimization of GS methods for photometric rendering quality. To address these bottlenecks, this work proposes a hybrid reconstruction pipeline, leveraging the strengths and benefits of each technique. The method exploits the accurate geometry of photogrammetry in well-textured regions and the GS capabilities to improve completeness and visual aspect in areas featuring non-collaborative surfaces. A fusion strategy is proposed to combine the two results into a single 3D model, presenting examples from two aerial and one terrestrial dataset.
Fabio Remondino, E. M. Farella, Gianluca Bertolasi et al.· The International Archives o...· 0 citations