Jul 2026· The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences· Vol XLIX-B2-2026, pp. 809-817· 0 citations· 3 references
TL;DR
The first results of the AI-based processing pipeline developed within the HERITALISE project are presented, applied to three multiscale case studies at the Reggia di Venaria Reale, demonstrating strong photorealistic rendering capabilities, particularly for complex material properties and geometrically challenging interiors, whilst highlighting current limitations for metric surveying applications.
Abstract
Abstract. The accurate digital documentation of Cultural Heritage (CH) assets demands workflows capable of integrating heterogeneous, multiscale datasets while preserving both geometric fidelity and radiometric completeness. This paper presents the first results of the AI-based processing pipeline developed within the HERITALISE project (Horizon Europe, 2025–2028), applied to three multiscale case studies at the Reggia di Venaria Reale (Turin, Italy): an outdoor-indoor UAV photogrammetric survey, a kinematic SLAM acquisition of a contemporary sculpture garden, and a close-range dataset of an 18th-century decorative artefact. 3D Gaussian Splatting (3DGS) is evaluated as a novel view synthesis method across all three scenarios, demonstrating strong photorealistic rendering capabilities, particularly for complex material properties and geometrically challenging interiors, whilst highlighting current limitations for metric surveying applications. A two-stage crack detection workflow, combining tile-based text-prompted segmentation with SAM3 and multiview ray-based reprojection onto the reconstructed mesh, is validated on UAV imagery, achieving an 84.9% ray–mesh intersection rate. Finally, a standardised evaluation framework is proposed, encompassing adaptive, scale-dependent geometric and radiometric metrics organised into reference-based and no-reference assessment scenarios, aggregated into a transparent synthetic quality score with three adaptive quality classes. The proposed methodology contributes toward a reproducible, sensor-agnostic standard for the assessment of AI-generated CH documentation products.
Abstract. The 3D documentation of complex scenes—characterized by restricted spaces, irregular geometries, and poor lighting—remains a significant challenge in cultural heritage. This study proposes a rapid data acquisition methodology based on the multi-sensor fusion of Terrestrial Laser Scanning (TLS) and Spherical Photogrammetry (SP). The approach was validated in two distinct complex environments: an ancient Egyptian rock-cut tomb (QH36, Aswan, Egypt) and a natural Iberian sanctuary cave (Cueva de la Lobera, Jaén, Spain). The methodology uses TLS to establish a high-precision geometric backbone, achieving registration errors below 0.5 cm. By extracting Ground Control Points (GCPs) directly from the TLS point cloud, the reliance on traditional total station surveying was significantly reduced, enhancing fieldwork efficiency. SP was implemented to obtain realistic textures and to support geometry by using a 360-degree multi-camera with integrated LED lighting, providing full spherical coverage and high-resolution textures. Results indicate that SP is at least six times faster than conventional photogrammetry. Furthermore, the use of TLS-derived meshes enabled advanced digital masking to remove non-interest objects (e.g., archaeological equipment) from the final models. While conventional photogrammetry remains the benchmark for fine architectural details, this research demonstrates that the TLS-SP fusion is the most viable solution for the rapid, high-accuracy documentation of constrained heritage sites. This hybrid workflow ensures geometric integrity while drastically reducing acquisition times, providing a robust framework for future archaeological and conservation projects.
A. Mozas-Calvache, José Luis Pérez-García, J. M. Gómez-López et al.· The International Archives o...· 0 citations
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
An integrated and open digital workflow for the survey, management, and reuse of complex architectural heritage, using Villa Aldrovandi-Mazzacorati as a case study is presented, offering a scalable and transferable framework for future 3D heritage initiatives across architectural and territorial contexts.
Marco Medici, Andrea Sterpin, Stefano Settimo et al.· Journal on Computing and Cul...· 0 citations
Cultural heritage faces accelerating threats from armed conflict and climate-induced disasters, generating emergency scenarios in which the window for documentation may be measured in hours. Traditional high-fidelity workflows such as Terrestrial Laser Scanning and survey-grade Structure-from-Motion campaign can be operationally incompatible with these constraints due to costly hardware and specialist-team requirements. Emergency image acquisition can be undertaken with consumer-grade equipment, while the same photographic dataset can subsequently support both an SfM-MVS mesh for metric and geometric analysis and a 3D Gaussian Splatting (3DGS) representation for view-dependent visual interpretation. This paper evaluates an accessible, entirely graphical workflow for producing these complementary outputs, with particular attention paid to the 3DGS stage and without requiring programming or command-line expertise. We develop a theoretical case drawing on Brandinian restoration theory and the international conservation framework, introducing the concept of informational amnesia, the irreversible loss of a site’s documentary record, as a distinct and undertheorized category of heritage failure. We further argue that the democratization of 3DGS documentation cannot be measured by licensing cost alone: a tool distributed as open-source code but requiring command-line expertise presents an effective accessibility barrier functionally equivalent to a commercial paywall. True accessibility requires installable, graphical software operable by non-specialist practitioners in the field. We present a proof-of-concept application of this accessible pipeline, namely DJI Mini 5 Pro, RealityScan for photogrammetric reconstruction, and Lichtfeld Studio for 3DGS generation, to the Fontana d’Ercole at the Reggia di Venaria Reale (UNESCO World Heritage, Turin, Italy). Visual results show photorealistic, navigable 3DGS surrogates of architecturally complex heritage assets achievable by non-specialist operators within hours of acquisition and establish the empirical foundation for future quantitative evaluation.
Alessio Martino, F. Chiabrando· The Heritage· 0 citations
Abstract. Accurate 3D reconstruction of Cultural Heritage (CH) assets remains a challenging task when scenes include complex geometries and non-Lambertian surfaces, such as dense vegetation, reflective ceramics, or polished materials, which often degrade the performance of traditional multi-view stereo (MVS) pipelines. This work investigates the potential of Mesh-In-the-Loop Gaussian Splatting (MILo), a recent extension of 3D Gaussian Splatting (3DGS) that integrates differentiable mesh extraction directly within the optimization process, enabling bidirectional consistency between volumetric and surface representations. The method is evaluated on three challenging CH datasets: a monumental Tilia tomentosa tree located in a UNESCO-listed urban garden, a reflective ceramic object from the Sarreguemines Earthenware Museum and the South Portal façade of the Notre-Dame Cathedral of Strasbourg. MILo-based reconstructions are compared against standard photogrammetric MVS results generated with Agisoft Metashape, using terrestrial laser scanner (TLS) point clouds as geometric reference. Quantitative accuracy assessment is performed through Multiscale Model-to-Model Cloud Comparison (M3C2), focusing on error distribution, standard deviation, outlier percentage, and preservation of fine-scale structures. Results indicate that while conventional MVS performs slightly better on stable architectural surfaces, MILo significantly improves reconstruction consistency for complex organic geometries, substantially reducing outliers and better preserving thin structures. These findings highlight the suitability of MILo for CH documentation scenarios characterized by challenging surface properties and intricate natural forms.
D. Billi, Chaimaa Delasse, Arnadi Murtiyoso et al.· The International Archives o...· 0 citations
Abstract. Monocular depth estimation (MDE) has reached notable maturity in computer vision, yet its application to UAV-based architectural heritage documentation remains underexplored. This study assesses whether the depth foundation model Depth Anything V2 can be transferred from terrestrial to aerial imagery. The analysis relies on MDE4BH, a benchmark of over 3,000 UAV images covering ten heterogeneous heritage scenarios (urban areas, façades, towers, villas, domes, and archaeological sites). Masked photogrammetric depth maps serve as metric reference for calibration, validation, and supervised retraining. Two baseline configurations are evaluated: a relative model with scene-specific linear rescaling and the direct application of the metric model. The rescaled relative model shows acceptable performance in several subsets, whereas the metric model exhibits systematic bias, weak consistency, and scale collapse due to domain shift between terrestrial training data and aerial acquisition geometry. To address these limitations, a two-step fine-tuning strategy is introduced, focusing on the decoder and regression head. The first stage uses mainly oblique UAV images; the second integrates oblique and nadir views to improve viewpoint generalization. The adapted model significantly reduces bias and enhances metric stability across the benchmark. However, residual errors remain spatially structured, with clustering and recurrent artefacts near object boundaries, multi-level roofs, and radiometrically heterogeneous surfaces. Although accuracy is still insufficient for demanding metric applications, the results support the use of MDE as a complementary source for thematic interpretation, scene understanding, robotics, navigation, and related tasks where strict geometric precision is not required.
F. Chiabrando, Francesca Gallitto, A. Lingua et al.· The International Archives o...· 0 citations