Aug 2026· The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences· Vol XLIX-B4-2026, pp. 759-765· 0 citations· 11 references
TL;DR
An adaptive neighborhood scale selection method for local PCA, designed for robust edge extraction from large-scale 3D scanned point clouds, and the use of octree-based spatial indexing ensures computational efficiency, making the method practical for field-based heritage conservation and structural analysis on consumer-grade hardware.
Abstract
Abstract. In recent years, as the digitization of cultural heritage has become increasingly important, 3D scanning has emerged as a key method for capturing accurate geometric structure. However, visually analyzing complex structures—such as intricate relief carvings and architectural details—remains challenging due to occlusion and measurement noise. This study proposes an adaptive neighborhood scale selection method for local PCA, designed for robust edge extraction from large-scale 3D scanned point clouds. The core contribution is an automatic scale selection framework that determines the optimal analysis scale by minimizing the variance of Eigentropy, enabling the most consistent representation of local geometric structures. To enhance the visibility of such structures, we further introduce a dual 3D edge extraction technique based on opacity gradation within a Stochastic Point-Based Rendering (SPBR) framework, allowing simultaneous visualization of both acute sharp edges and rounded soft edges while mitigating occlusion. The effectiveness of the proposed method is demonstrated through case studies on the reliefs of Borobudur Temple and the Red Brick Storehouse. Experimental results show that the adaptive approach significantly improves the visibility of overlapping structures compared to conventional fixed-scale methods. In addition, the use of octree-based spatial indexing ensures computational efficiency, making the method practical for field-based heritage conservation and structural analysis on consumer-grade hardware.
Abstract. While virtual reality (VR) has been increasingly adopted for the digital archiving of cultural heritage, most existing systems focus on visual appreciation rather than structural analysis. To address this limitation, this study proposes a transparent VR visualization system designed to enhance the recognition of structural features in 3D scanned point clouds of historic architecture. Using a large-scale point cloud dataset of the main hall of Tamaki Shrine, which is part of the UNESCO World Heritage site “Sacred Sites and Pilgrimage Routes in the Kii Mountain Range,” the data were initially processed using Poisson disk sampling to optimize real-time rendering throughput while preserving geometric fidelity. Structural salience was subsequently quantified through principal component analysis (PCA), employing linearity for sharp-edge extraction and the change of curvature for soft-edge representation. To alleviate the occlusion inherent in opaque point cloud rendering, we implemented a custom alpha-blending shader in Unity. This enabled two types of transparent feature-enhanced visualization: binarization for highlighting sharp edges and opacity gradation for visualizing soft-edge regions. The developed system, operated with a Meta Quest Pro, provides distinct viewing and analysis modes. Experimental results demonstrate that combining feature enhancement with opacity control improves the visibility of internal depth relationships and point-density variations in complex wooden architecture, thereby supporting structural understanding that is difficult to achieve with conventional rendering.
Taishi Matsuo, Satoshi Takatori, Liang Li et al.· The International Archives o...· 0 citations
Abstract. Three-dimensional (3D) scanning is widely used to preserve cultural heritage as large-scale point clouds. While these datasets contain rich geometric information, transparent visualization of such massive point clouds often suffers from visual clutter and reduced clarity, particularly when both external and internal structures are involved. Previous work resolved the problem of normal orientations, laying the foundation for robust shading in transparent visualization. Building on this foundation, this paper introduces a novel method of adaptive opacity control for region highlighting, which interprets shading as a distribution of opacity. By adjusting the lighting direction, effective opacity can be locally controlled without modifying the original point cloud data. This mechanism enables selective highlighting of user-specified regions, enhances the visibility of complex structures, while also allowing interactive dynamic shading by continuously changing the lighting direction. The effectiveness of the proposed method is demonstrated using culturally significant heritage point clouds, including UNESCO World Heritage sites, where intricate internal structures can be more clearly analyzed. Beyond cultural heritage, the proposed method is also applicable to modern architectural and other large-scale 3D scanned objects with similarly complex forms.
Hatsuki Nojiri, Satoshi Takatori, Liang Li et al.· ISPRS Annals of the Photogra...· 0 citations
Abstract. This paper presents a semi-automated pipeline for extracting architectural plans from terrestrial LiDAR point clouds of archaeological sites characterized by irregular geometries and significant surface degradation. The proposed workflow converts dense three-dimensional data into simplified two-dimensional representations by combining geometric alignment, expert-guided cross-section selection, feature detection using Intrinsic Shape Signatures (ISS), region-based segmentation, and polyline simplification. The method is specifically designed to handle rock-cut tomb environments, where erosion, noise, and non-planar surfaces complicate conventional Scan-to-Plan approaches. The pipeline is evaluated on six tombs from the Sheikh Said necropolis in Middle Egypt, covering a range of architectural configurations and preservation states. Results demonstrate that the method efficiently produces CAD-compatible outlines that capture the dominant structural geometry while significantly reducing manual drafting time. However, the accuracy of the generated plans depends on surface conditions, with degraded or ornamented areas introducing geometric artifacts that require expert refinement. The proposed approach provides a robust geometric baseline for archaeological documentation and highlights the potential of human-in-the-loop workflows for complex heritage environments.
Marianna Bartrick-Krana, Roberto de Lima-Hernandez, A. Vandesande et al.· The International Archives o...· 0 citations
Abstract. Point-cloud-derived 2D projections enable generating unlimited virtual views for indoor scene analysis and dataset creation. However, projecting irregular 3D samples onto a dense image grid commonly produces void pixels due to sparsity, occlusions, and incomplete scan coverage. These projection-induced artifacts degrade the visual fidelity of rendered images and limit their usefulness in downstream image-based workflows. This study investigates void-filling strategies tailored to point-cloud-generated RGB projections and provides a comparative evaluation of three representative approaches: (i) K-nearest neighbor (KNN) interpolation with KD-Tree accelerated neighbor search, (ii) a rule-based neighborhood method (NNRule) that adapts filling behavior using local variability to preserve edges, and (iii) a mask-normalized Gaussian-weighted propagation method that diffuses valid color information into void regions. Experiments were conducted on multi-view perspective projections generated from Stanford Large-Scale 3D Indoor Spaces Dataset (S3DIS) Area 3, totalling 5,520 images. Restoration quality was assessed using standard pixel-level metrics such as MAE, RMSE, PSNR, and SSIM. Quantitative results show that Gaussian-weighted propagation achieved the best overall performance, followed by NNRule, while KNN performed weakest numerically. Qualitative comparisons further indicate that KNN produces the most visually realistic texture appearance, whereas diffusion-based filling is softened fine details. Finally, the study establishes a practical baseline that enables both academic researchers to advance point-cloud-to-image restoration without relying on paired RGB datasets and industrial practitioners to deploy light weight void-filling pipelines in real-world applications such as digital twins, indoor robotics, facility management, and augmented reality.
M. Chowdhury, S. Han, J. Ma· The International Archives o...· 0 citations
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
Photogrammetric 3D city models contain detailed planimetric and elevation information that supports urban visualization and low-altitude applications. However, the direct dissemination of high-accuracy models may expose sensitive geometric measurements. Existing protection methods mainly focus on conventional encryption, coordinate scrambling, or two-dimensional data perturbation and do not adequately balance geometric accuracy degradation with the visual usability of textured 3D meshes. This study proposes a controlled geometric deformation method that processes the planimetric and elevation components independently. In the horizontal domain, a normalized Sigmoid function generates smooth, bounded, and spatially varying coordinate displacements. In the vertical domain, a normalized deformation function combines global elevation stretching with amplitude-constrained sine-wave superposition. The sine-wave parameters are generated using a seed-sensitive hybrid cascaded chaotic system, producing reproducible but model-dependent nonlinear deformation patterns. During processing, the mesh connectivity, face indices, texture coordinates, texture images, and material relationships remain unchanged. The method was evaluated using low-rise and high-rise photogrammetric 3D scenes with different horizontal extents and elevation characteristics. Under the selected 10 m planimetric and 5% elevation settings, the mean planimetric displacements were 10.474 and 10.045 m, while the relative elevation deformations were 5.01% and 5.30%, respectively. Both datasets maintained monotonic elevation relationships and achieved 100% direction consistency. Their spatial-shape coefficients deviated from the corresponding reference values by only 0.02% and 1.33%. The results demonstrate that the proposed method provides controllable and spatially continuous geometric deformation while maintaining mesh connectivity, overall morphology, and visual interpretability. It can therefore serve as a practical pre-processing approach for the risk-reduced dissemination and non-measurement-oriented visualization of photogrammetric 3D city models.
Siyuan Zou, Zibo Xu, Yiwen Wang et al.· Remote Sensing· 0 citations