Three-dimensional modeling and morphological characterization of pavement texture using a self-developed laser scanning device

Abstract. Landscape restoration in semi-arid environments demands not only effective interventions but also communicative approaches that make complexity legible and foster ecological literacy. Immersive technologies such as Virtual Reality (VR) transform geospatial data into navigable environments, making processes tangible and transferable experiences. This study translates a remote restoration site in the Murcia region of Spain, characterized by limited accessibility and harsh conditions, into an immersive Virtual Reality geovisualization. A UAV structure-from-motion survey produced a high-resolution textured model that was geospatially situated with BlenderGIS (ESRI imagery over a 30 m digital elevation model) and then reconstructed in Twinmotion under a calibrated HDRI skydome. The scene is engineered for room-scale use with teleport-only locomotion and a uniform down-scale that enables hand-scale inspection of swales, ponds, ground cover, tree rows, and micro-topography. Three access points organize short narrated sequences that guide users from landscape overview to near-field readings, converting mesh into meaning. A pilot on Meta Quest 3 yielded encouraging signals: high presence and perceived realism, low discomfort, positive self-reported competence, and intent to re-engage. The pilot also surfaced technical priorities, including tighter blending between the VR layers, and bias-aware context. The contribution is a reproducible workflow that combines geospatial context and proxemic design to support spatial reasoning, knowledge transfer, and public communication. Strategically, the module offers a path toward a participatory, education-ready XR tool for regenerative practice, and a future platform for stakeholders, to support cognitive spatial reasoning and field-digital decision-making.

Application of convolutional neural networks for nasofacial reconstruction.

Object detection in buffet-style environments is highly challenging due to densely stacked tableware, frequent occlusions, strong illumination reflections, and substantial visual similarity across categories, all of which undermine the robustness of existing detectors. To address these issues, this paper proposes an improved real-time detection transformer–based model with a lightweight design while significantly enhancing multi-scale feature representation. First, a re-parameterized stem module is introduced to strengthen shallow texture extraction with negligible computational overhead. Second, a dynamic multi-kernel refinement module is developed to enrich directional texture modeling and cross-scale semantic aggregation. Furthermore, a heterogeneous-kernel feature pyramid network is constructed by integrating adaptive multi-scale fusion, multi-kernel fusion nodes, and a lightweight upsampling strategy to improve cross-level feature consistency and mitigate aliasing caused by conventional upsampling. Experimental results on a self-constructed buffet-scene dataset demonstrate that the proposed method improves mAP50 and mAP50:95 by 2.6% and 1.9%, respectively, while reducing parameters and GFLOPs by 42.6% and 42.3%, and increasing inference speed to 103.1 FPS. On Dota v1.0 and SkyFusion data sets, the small target detection ability has also been improved. The substantial reductions in computation and model size further confirm the effectiveness and practical value of the proposed approach for complex catering scenarios.

Accurate six-degree-of-freedom (6-DoF) camera pose estimation is essential for augmented reality, robotics navigation, and indoor mapping. Existing pipelines often depend on detailed floorplans, strict Manhattan-world priors, and dense structural annotations, which lead to failures in ambiguous room layouts where multiple rooms appear in a query image and their boundaries may overlap or be partially occluded. We present Render-Rank-Refine, a two-stage framework operating on coarse semantic meshes without requiring textured models or per-scene fine-tuning. First, panoramas rendered from the mesh enable global retrieval of coarse pose hypotheses. Then, perspective views from the top-k candidates are compared to the query via rotation-invariant circular descriptors, which re-ranks the matches before final translation and rotation refinement. Our method increases camera localization accuracy compared to the state-of-the-art SPVLoc baseline by reducing the translation error by 40.4% and the rotation error by 29.7% in ambiguous layouts, as evaluated on the Zillow Indoor Dataset. In terms of inference throughput, our method achieves 25.8–26.4 QPS, (Queries Per Second) which is significantly faster than other recent comparable methods, while maintaining accuracy comparable to or better than the SPVLoc baseline. These results demonstrate robust, near-real-time indoor localization that overcomes structural ambiguities and heavy geometric assumptions.

Pine wilt disease (PWD), characterized by rapid transmission and high pathogenicity, causes severe ecological and economic damage worldwide. Early detection is critical for curbing its spread, yet the concealed symptoms and minute lesions make it difficult for existing models to balance high accuracy with lightweight efficiency in complex forest environments. To address these challenges, this study proposes a lightweight detection model named LE-PWDNet. A total of 41,568 high-resolution UAV images were collected from diverse field scenarios to construct a standardized dataset covering four infection stages, providing comprehensive support for model training and evaluation. The model is built upon the DEIM training paradigm to enhance the utilization of positive samples for small-target detection. To strengthen multi-scale texture modeling of early lesions, a Wavelet Detail Attention Convolution (WDAConv) is designed. A ConvFFN module is introduced to mitigate the attenuation of high-frequency details, thereby improving robustness under complex backgrounds. A CGAFusion module is developed to reduce false positives caused by background noise. Furthermore, an Edge-Dilated Sampling-Point Generator (DySample-E) is incorporated to dynamically adjust the upsampling process, enhancing the ability to capture early micro-lesions. Experimental results demonstrate that, with only 5.64M parameters and approximately 7 GFLOPs, LE-PWDNet achieves an AP50 of 83.8% for early-stage lesion detection and an overall AP50 of 90.2%, outperforming existing mainstream models. This study provides a feasible solution for building intelligent and low-cost early-warning systems for forest diseases and highlights the broad application potential of the proposed framework in forestry and other ecological monitoring scenarios.

Recent advances in image acquisition and scene reconstruction have enabled the generation of high-quality structural urban scene geometry, given sufficient site information. However, current capture techniques often overlook the crucial importance of texture quality, resulting in noticeable visual artifacts in the textured models. In this work, we introduce the urban geometry and texture co-capture problem under limited prior knowledge before a site visit. The only inputs are a 2D building contour map of the target area and a safe flying altitude above the buildings. We propose an innovative aerial path planning framework designed to co-capture images for reconstructing both structured geometry and high-fidelity textures. To evaluate and guide view planning, we introduce a comprehensive texture quality assessment system, including two novel metrics tailored for building facades. Firstly, our method generates high-quality vertical dipping views and horizontal planar views to effectively capture both geometric and textural details. A multi-objective optimization strategy is then proposed to jointly maximize texture fidelity, improve geometric accuracy, and minimize the cost associated with aerial views. Furthermore, we present a sequential path planning algorithm that accounts for texture consistency during image capture. Extensive experiments on large-scale synthetic and real-world urban datasets demonstrate that our approach effectively produces image sets suitable for concurrent geometric and texture reconstruction, enabling the creation of realistic, textured scene proxies at low operational cost.

Digital curation of formalin-preserved heart specimens via 3D photometric scanning: A report on recent archiving techniques and optimizations.

Due to the stochastic nature of textured surfaces, in-situ quality monitoring for texture-related defects using statistical process monitoring (SPM) is important yet challenging in academic research and industrial applications. This article presents an in-situ EWMA monitoring scheme based on the likelihood ratio test to quantify and detect unexpected global shifts in textured surfaces. We employ Gradient Boosting Regression Trees to implicitly characterise the joint distribution of textile image pixels and for the texture modelling. With the limited number of Phase I samples, the proposed scheme with a data-driven control limit algorithm can estimate the distribution of the charting statistics via the kernel density estimation (KDE) method, continuously update the probability limits at each time point during the monitoring phase and implement the dynamic monitoring to identify defective surfaces with a relatively satisfying in-control monitoring performance. The simulated stochastic experiments confirm the advantage of the proposed method. Also, a real layerwise images monitoring case based on Fused Deposition Modeling from Additive Manufacturing (AM) is provided.

This study addresses the common challenges in medical image segmentation and recognition, including boundary ambiguity, scale variation, and the difficulty of modeling long-range dependencies, by proposing a unified framework based on a hierarchical attention mechanism. The framework consists of a local detail attention module, a global context attention module, and a cross-scale consistency constraint module, which collectively enable adaptive weighting and collaborative optimization across different feature levels, thereby achieving a balance between detail preservation and global modeling. The framework was systematically validated on multiple public datasets, and the results demonstrated that the proposed method achieved Dice, IoU, Precision, Recall, and F1 scores of 0.886, 0.781, 0.898, 0.875, and 0.886, respectively, on the combined dataset, outperforming traditional models such as U-Net, Mask R-CNN, DeepLabV3+, SegNet, and TransUNet. On the BraTS dataset, the proposed method achieved a Dice score of 0.922, Precision of 0.930, and Recall of 0.915, exhibiting superior boundary modeling capability in complex brain MRI images. On the LIDC-IDRI dataset, the Dice score and Recall were improved from 0.751 and 0.732 to 0.822 and 0.807, respectively, effectively reducing the missed detection rate of small nodules compared to traditional convolutional models. On the ISIC dermoscopy dataset, the proposed framework achieved a Dice score of 0.914 and a Precision of 0.922, significantly improving the accuracy of skin lesion recognition. The ablation study further revealed that local detail attention significantly enhanced boundary and texture modeling, global context attention strengthened long-range dependency capture, and cross-scale consistency constraints ensured the stability and coherence of prediction results. From a medical economics perspective, the proposed framework has the potential to reduce diagnostic costs and improve healthcare efficiency by enabling faster and more accurate image-based clinical decision-making. In summary, the hierarchical attention mechanism presented in this work not only provides an innovative breakthrough in mathematical modeling but also demonstrates outstanding performance and generalization ability in experiments, offering new perspectives and technical pathways for intelligent segmentation and recognition in medical imaging.

The domain of forensic science, dermatology, and regenerative medicine critically relies on the precise replication of human skin details. Nevertheless, conducting on-site analysis poses challenges due to the stringent requirements for stability, accuracy, and the use of safe imaging materials. Current skin imaging methodologies are hindered by the inherent limitations of their hardware components, particularly when it comes to capturing the intricate, micrometer-scale textures of human skin. To address these challenges, we develop a low-cost (<$800), portable nanofiber-based imaging technique (NFIT) using CsPbBr3@HPβCD luminescent nanofibers. NFIT achieves in-situ, multi-regional imaging with ultrahigh-resolution (1450 dpi) and micron-scale similarity (93.24 ± 4.6%), capturing intricate details from sweat pores to large skin areas. Its non-contact design eliminates chemical pre/post-treatments, ensuring safety, hygiene and ease of use. NFIT demonstrates robustness and reliability as it maintains clear imaging under extreme temperature (−50 °C to +50 °C) and over extended periods (Level 3 ≥ 81 days, Level 2 ≥ 108 days). An algorithm was developed to support 3D skin texture model reconstruction, offering a transformative solution for forensic evidence analysis, dermatological assessments, and personalized medicine.

Abstract. Feature matching is a necessary step for many computer vision and photogrammetry applications such as image registration, structure-from-motion, and visual localization. Classical handcrafted methods such as SIFT feature detection and description combined with nearest neighbour matching and RANSAC outlier removal have been state-of-the-art for mobile mapping cameras. With recent advances in deep learning, learnable methods have been introduced and proven to have better robustness and performance under complex conditions. Despite their growing adoption, a comprehensive comparison between classical and learnable feature matching methods for the specific task of semantic 3D building camera-to-model matching is still missing. This submission systematically evaluates the effectiveness of different feature-matching techniques in visual localization using textured CityGML LoD2 models. We use standard benchmark datasets (HPatches, MegaDepth-1500) and custom datasets consisting of facade textures and corresponding camera images (terrestrial and drone). For the latter, we evaluate the achievable accuracy of the absolute pose estimated using a Perspective-n-Point (PnP) algorithm, with geometric ground truth derived from geo-referenced trajectory data. The results indicate that the learnable feature matching methods vastly outperform traditional approaches regarding accuracy and robustness on our challenging custom datasets with zero to 12 RANSAC-inliers and zero to 0.16 area under the curve. We believe that this work will foster the development of model-based visual localization methods. Link to the code: https://github.com/simBauer/To_Glue_or_not_to_Glue

A 3D reconstruction pipeline for generating textured models of large-scale architectural heritage

Photogrammetry is a mapping method that utilizes photographic images to generate three-dimensional (3D) models. In this study, the photogrammetry method is used to create a 3D model of the STMIK TIME campus buildings to document and visualize the structures digitally. The process of creating the 3D model begins with image acquisition using a digital camera, followed by processing with photogrammetry software to generate point clouds, meshes, and textured models. The final result of this research is a 3D model of the STMIK TIME campus, which can be used for various purposes such as architectural planning, building preservation, and visual simulation. Based on the evaluation conducted, the resulting 3D model demonstrates a high level of accuracy in representing the actual building structure. Thus, the photogrammetry method has proven to be an effective technique for creating 3D building models at a more affordable cost compared to conventional 3D modeling techniques.

The electromagnetic induction (EMI) and frequency domain reflectometry (FDR) sensors, which measure the soil apparent electrical conductivity (ECa) in situ, have emerged as efficient and rapid tools for the indirect assessment of soil salinity, conventionally determined by the electrical conductivity of the saturated soil paste extract (ECe). However, the limitations of applying a single soil sensor and the ECa dependence on multiple soil properties, such as soil moisture and texture, can hinder the interpretation of ECe, whereas selecting the most appropriate set of sensors is challenging. To address these issues, this study explored the prediction ability of a noninvasive EM38-MK2 (EMI) and a capacitance dielectric WET-2 probe (FDR) in assessing topsoil salinity and texture within 0–30 cm depth across diverse soil and land-use conditions in Laconia, Greece. To this aim, multiple linear regression models of laboratory-estimated ECe and soil texture were constructed by the in situ measurements of EM38-MK2 and WET-2, and their performances were individually evaluated using statistical metrics. As was shown, in heterogeneous soils with sufficient wetness and high salinity levels, both sensors produced models with high adjusted coefficients of determination (adj. R2 &gt; 0.82) and low root mean square error (RMSE) and mean absolute error (MAE), indicating strong model fit and reliable estimations of topsoil salinity. For the EM38-MK2, model accuracy improved when clay was included in the regression, while for the WET-2, the soil pore water electrical conductivity (ECp) was the most accurate predictor. The drying soil surface was the greatest constraint to both sensors’ predictive performances, whereas in non-saline soils, the silt and sand were moderately assessed by the EM38-MK2 readings (0.49 &lt; adj. R2 &lt; 0.51). The results revealed that a complementary use of the contemporary EM38-MK2 and the low-cost WET-2 could provide an enhanced interpretation of the soil properties in the topsoil without the need for additional data acquisition, although more dense soil measurements are recommended.

ABSTRACT Ming and Qing historical buildings are key components of China’s cultural heritage, characterized by refined craftsmanship and unique spatial layouts. However, UAV restrictions and heritage protection policies often limit the availability of comprehensive 3D data. To address this, we propose shp2gml, a semantic 3D modeling method that integrates building footprints, façade imagery and unstructured web text under data- constrained conditions. First, domain knowledge and deep learning are combined to extract building features from multi-source data. Then, by analyzing architectural characteristics and applying domain-specific rules, a modeling algorithm is designed to generate semantic 3D models. Symmetry priors are further introduced to infer missing elements and enhance texture-to-geometry mapping. Experiments show that shp2gml produces LoD0 to textured LoD3 models, achieving an overall F1-score of 78% and >0.80 accuracy for key entities in NER task. Compared with existing models, roof-type accuracy reaches 100% and door/window completeness improves to 83%, demonstrating its effectiveness for cultural heritage modeling and GIS-based applications. This study not only advances semantic 3D modeling for architectural heritage but also offers new perspectives for interdisciplinary research in GIS and heritage digitalization.

Protection of coastal cultural heritage is among the most urgent global priorities, as these sites face increasing threats from climate change, sea level rise, and human activity. This study emphasises the value of innovative geospatial tools and data ecosystems for timely risk assessment. The role of land administration systems, geospatial documentation of coastal cultural heritage sites, and the adoption of innovative techniques that combine various methodologies is crucial for timely action. The coastal management infrastructure in Greece is presented, outlining the key public authorities and national legislation, as well as the land administration and geospatial ecosystems and the various available geospatial ecosystems. We profile the Hellenic Cadastre and the Hellenic Archaeological Cadastre along with open geospatial resources, and introduce TRIQUETRA Decision Support System (DSS), produced through the EU’s Horizon project, and a Digital Twin methodology for hazard identification, quantification, and mitigation. Particular emphasis is given to the role of Digital Twin technology, which acts as a continuously updated virtual replica of coastal cultural heritage sites, integrating heterogeneous geospatial datasets such as cadastral information, photogrammetric 3D models, climate projections, and hazard simulations, allowing for stakeholders to test future scenarios of sea level rise, flooding, and erosion, offering an advanced tool for resilience planning. The approach is validated at the coastal archaeological site of Aegina Kolona, where a UAV-based SfM-MVS survey produced using high-resolution photogrammetric outputs, including a dense point cloud exceeding 60 million points, a 5 cm resolution Digital Surface Model, high-resolution orthomosaics with a ground sampling distance of 1 cm and 2.5 cm, and a textured 3D model using more than 6000 nadir and oblique images. These products provided a geospatial infrastructure for flood risk assessment under extreme rainfall events, following a multi-scale hydrologic–hydraulic modelling framework. Island-scale simulations using a 5 m Digital Elevation Model (DEM) were coupled with site-scale modelling based on the high-resolution UAV-derived DEM, allowing for the nested evaluation of water flow, inundation extents, and velocity patterns. This approach revealed spatially variable flood impacts on individual structures, highlighted the sensitivity of the results to watershed delineation and model resolution, and identified critical intervention windows for temporary protection measures. We conclude that integrating land administration systems, open geospatial data, and Digital Twin technology provides a practical pathway to proactive and efficient management, increasing resilience for coastal heritage against climate change threats.

Abstract. Digitalizing cultural heritage demands accurate 3D models for documentation, conservation, and restoration, focusing on both geometry and texture. For medium-sized objects, 3D scanning commonly provides accurate geometry, while photogrammetry excels at capturing texture. Therefore, a hybrid workflow is often used. However, on-site acquisition of complex objects, such as the polychrome 'Saint Catherine of Alexandria' sculpture in Jaén, Spain, presents significant radiometric challenges due to variable lighting and shiny surfaces. This study details a two-phase methodology to create a photorealistic 3D model of this carving. The first phase involved capturing geometry with a structured light scanner and acquiring photogrammetric images using a conventional camera. These datasets were then fused, combining the 3D scan and oriented photogrammetric block to obtain the initial 3D model. Additionally, a colorimeter simultaneously measured true colour values of seven distinct chromatic segments (e.g., gold tunic, face, shoes) to address the carving's challenging reflectivity. The second phase focused on radiometric correction. Images were segmented using a divided 3D mesh and depth maps generated for each segment. Each segment's RGB values were then adjusted to match the colorimeter's average reference value for that specific segment. This zonal correction strategy ensured colour homogeneity. The resulting 3D model, textured with these corrected images, showed a significant improvement in colour realism. The average RGB distance between colorimeter measurements and the model's texture was substantially reduced using this approach. This preliminary study demonstrates the potential and robustness of this method for achieving accurate colour fidelity in 3D models, even under challenging on-site conditions.

Ensuring dam safety requires continuous monitoring to detect structural anomalies such as cracks, displacements, and seepage. This study explores aerial surveys using Unmanned Aerial Vehicles combined with open-source software to enhance dam monitoring capabilities. The methodology enables efficient inspections in difficult-access areas and supports early identification of potential failure indicators. High-resolution aerial imagery was processed to generate detailed three-dimensional representations including point clouds, orthophotos, and textured models. A novel Ground Control Points Finder tool was developed to automate identification and georeferencing, significantly improving spatial accuracy and survey efficiency. The study systematically presents fourteen failure modes detectable through aerial mapping, including piping, hydraulic fracturing, foundation instability, and freeboard loss, with their characteristic surface manifestations. The approach was validated using Lapa Dam as a case study through two aerial surveys conducted ten months apart. Analysis using open-source software successfully identified surface deformations, settlements, and vegetation changes, demonstrating the methodology’s effectiveness in detecting structural anomalies associated with potential failure modes. The results confirm that integrating aerial surveys with open-source processing tools offers a low-cost solution to complement dam safety assessments. The approach improves early detection of various failure modes, reduces inspector exposure to hazardous conditions, and supports informed decisions in monitoring embankment dams through accessible technology.

Abstract. This study, conducted as part of the Rhineland Castles 3D Modeling Project, addresses the problem of color inconsistencies in 3D reconstructions generated from diverse imagery sources, including drone footage and terrestrial photography. Variations in lighting conditions, camera sensors and acquisition settings often result in photometric discrepancies that degrade the quality of the textured 3D model. Two datasets from Ramstein and Oedenbourg Castles in France were analysed, each presenting characterised by distinct photometric conditions. The Ramstein dataset exhibited relatively uniform lighting, allowing the application of statistical color transfer methods. Among the methods evaluated, Mean-Lab Transfer produced the most photometrically consistent and visually faithful results. In contrast, the Oedenbourg dataset presented significant photometric variability, characterised by extreme variations in saturation, luminosity, and contrast. To effectively manage these challenging conditions Here, a two-step enhancement approach was adopted, combining gamma correction (in HSV color space) and with CLAHE to balance brightness and preserve color identity. This paper details the applied methodologies, evaluates their effectiveness in achieving color consistency, and highlights the importance of adopting emphasizing the need for dataset-specific processing pipelines.