Search Results (1,214)

Dynamic scene reconstruction has achieved significant milestones with the advent of 3D Gaussian Splatting (3DGS). However, extending this technology from geometric reconstruction to semantic understanding in dynamic environments remains a challenge. Existing methods often rely on external 2D trackers, which lead to temporal inconsistencies and semantic drift, or suffer from the high computational costs of high-dimensional feature fields. In this paper, we propose a novel framework, Gaussian Semantic Segmentation based on Color and Shape Deformation Fields (GSSBC), to address these issues. Building upon our GBC dynamic scene representation, we bind learnable semantic features to deformable Gaussian primitives. We introduce a spatiotemporal contrastive learning strategy guided by the Segment Anything Model (SAM) to enforce semantic consistency without explicit tracking. Furthermore, we employ a density-based clustering algorithm with label propagation to extract discrete object entities efficiently. Experimental results on the HyperNeRF and Neu3D datasets demonstrate that our method achieves superior segmentation accuracy and spatiotemporal stability compared to state-of-the-art approaches, enabling effective semantic understanding in complex dynamic scenes. Full article

Background/Objectives: Bud sports (somatic mutations) offer a quick way to develop new bougainvillea varieties by altering specific traits while keeping the desirable genetic background of the original cultivar. However, we still lack a comprehensive understanding of their genomic architecture and the molecular mechanisms behind their formation. This study aimed to characterize the population genomic characteristics of bud sports derived from the commercial variety Bougainvillea × buttiana ‘Miss Manila’. Methods: We employed genotyping by sequencing (GBS) on 39 accessions, including 27 bud sports and 12 conventional varieties. Population genomic analyses, such as principal component analysis (PCA), phylogenetic reconstruction, ADMIXTURE, and diversity statistics (π, He, Tajima’s D), were performed on 64,810 high-quality SNPs. Genome-wide scans for differentiation (FST) and selective sweeps (XP-CLR) were also conducted. Results: Bud sports showed significantly lower genetic diversity (π and He) than conventional varieties, which matches their clonal origin. PCA, phylogenetic, and ADMIXTURE analyses (optimal K = 4) revealed clear genetic differentiation and distinct population structures between the two groups. The bud sport population possessed fewer private alleles and a less negative Tajima’s D value. Genomic scans identified regions under selection in bud sports, with functional annotation pointed to genes involved in ubiquitin-mediated proteolysis and RNA transport. Notably, Bou_119143 (UDP-rhamnose rhamnosyltransferase 1) showed a high mutation frequency specifically in bud sports. Conclusions: We provide the first population-genomic evidence that bud sports of ‘Miss Manila’ are genetically distinct clonal lineages, shaped by somatic mutation and selection. These findings support bud sports as efficient sources for germplasm innovation. The identified genomic regions and candidate genes lay a foundation for future marker-assisted selection and molecular breeding in bougainvillea. Full article

The accurate interpretation of structural edges requires distinguishing geometry-driven discontinuities from reflectance- and illumination-induced variations. Conventional photogrammetric pipelines rely primarily on radiometric and geometric cues, which often lack physical interpretability under complex material and lighting conditions. This study proposes a photogrammetry–polarimetry fusion framework for physics-guided semantic classification of 3D structural edges. Radiometric, geometric, and polarimetric features are integrated within a noise-normalized representation to enable modality-independent interpretation. A rule-based classification scheme is introduced to assign edges to physically meaningful categories, including geometric, material, specular, illumination, and polarization-driven phenomena. The method is evaluated on a calibrated geometric object and a cultural heritage statue. Results show that polarization provides complementary information that reduces ambiguity between geometry-driven and reflectance-driven edge responses while preserving the underlying reconstructed geometry. On the calibrated dataset, edge detection achieves 88.4% precision, 95.5% recall, and an F1-score of approximately 0.92. Multi-view integration further improves the completeness of geometry-dominant 3D edges. The proposed framework introduces a physics-guided semantic sensing layer for multi-modal 3D perception, enabling more robust and interpretable structural analysis in photogrammetric workflows. Full article

Background/Objectives: Transcatheter ablation assisted by three-dimensional (3D) electroanatomical mapping (EAM) is the elective treatment for atrioventricular nodal reentrant tachycardia (AVNRT) in children and adolescents. In this population of patients, the most frequently employed EAM strategies are the low-voltage bridge (LVB) strategy and sinus rhythm propagation mapping (SRPM). However, the exact pathophysiology and anatomy of the AVNRT reentrant circuits are still poorly understood. The aim of this study was to investigate the relationship between SRPM and LVB and to shed light on nodal physiology in children and adolescents affected by AVNRT. Methods: We retrospectively collected data on pediatric patients who underwent cryoablation for AVNRT assisted by high-density 3D EAM by using the LVB strategy; maps were reviewed by two independent electrophysiologists and the SRPM was described. SRPM was defined as typical when only one collision area was identified and atypical whenever either no or ≥ two collision areas were localized. Results: Twenty-eight consecutive patients (11.3 ± 3.3 years) were enrolled. All procedures were acutely successful. Overall, atypical SRPM was present in 10 patients (35.7%), and it did not correlate with the presence of multiple SPs or electrophysiological data. Moreover, we observed an imperfect concordance between SRPM and LVB (only in 10/18 patients). When SRPM and LVB were assessed in different locations, the LVB identified the effective cryoablation site in more cases than SRPM (4/8 vs. 1/8). Lastly, in cases of double collision, one collision area co-localized with the LVB and the effective cryoablation spot, whereas the other was located superiorly, closer to the His bundle. Conclusions: Atypical sinus rhythm propagation in the Koch’s triangle is a frequent finding in pediatric AVNRT patients. In this series, LVB showed closer concordance with the successful cryolesion site than retrospectively reconstructed SRPM. Full article

In the Industrial Internet of Things (IIoT), vision-based industrial detection technology is crucial in the production process and can be used in many smart manufacturing applications, such as automated production control and Non-Destructive Evaluation (NDE). To enable timely and accurate decision-making, the network must transmit product status information to the server under stringent requirements of ultra-reliability and low latency. However, traditional pixel-centric industrial image transmission consumes additional bandwidth, and existing deep learning-based semantic communication systems rely on costly manual annotations. To overcome these limitations, this paper proposes a novel object-centric semantic communication framework based on improved slot attention for Multiple-Input Multiple-Output (MIMO) transmission in a 6G smart manufacturing scenario. First, we propose an improved slot attention method based on unsupervised learning for real-world manufacturing image datasets. The proposed method decouples complex industrial images into different object instances, each corresponding to an independent semantic component slot, effectively isolating task-related visual targets from redundant backgrounds. Furthermore, we propose a priority-based semantic transmission strategy. By quantifying the task-relevant importance of each semantic slot and jointly matching MIMO sub-channels, our method optimizes industrial image transmission streams, ensuring the reliable transmission of the important semantic information. Extensive simulation results demonstrate that the proposed framework significantly enhances communication transmission efficiency. Even under constrained bandwidth ratios and a low Signal-to-Noise Ratio (SNR), our framework achieves superior visual reconstruction quality and improves the Peak Signal-to-Noise Ratio (PSNR) by 4.25 dB compared to existing benchmarks. Full article

Accurate alignment of real-world object poses with their virtual counterparts using sensors, e.g. cameras, is essential for consistent interaction in mixed-reality systems. However, objects can undergo abrupt, untracked movements during periods when a tracking system is inactive, e.g., overnight, causing stored pose records to become inconsistent with the real scene and breaking user interaction in the virtual environment. Off-the-shelf 3D reconstruction networks such as MASt3R (Matching and Stereo 3D Reconstruction) method provide metrically scaled 3D point maps and pixel correspondences, but they are trained on static scenes and therefore fail to produce reliable object correspondences when the object has moved. We propose a robust pipeline that combines MASt3R’s metrically scaled 3D outputs with a background-based alignment strategy to recover and apply the true pose change of moved objects. Our method first segments foreground and background and extracts 3D background point sets for a reference day and a current day. An affine transformation between these background point sets is estimated via a standard registration technique and used to express the current-day object 3D coordinates in the reference coordinate frame. Within that unified frame we compute the object pose change and apply the resulting transform to the virtual object, restoring real–virtual consistency. Experiments on real scenes demonstrate that the proposed approach reliably corrects pose misalignments introduced during inactive periods and substantially improves over applying MASt3R alone, thereby enabling restored and consistent user interaction in the virtual environment. Full article

Background/Objectives: Bioceramic scaffolds with complex geometries and customized mechanical and biological properties can now be produced via 3D printing, revolutionizing dental and maxillofacial tissue engineering. This review discusses the recent progress in 3D printing technologies applied to bioceramic scaffolds for dental and maxillofacial reconstruction. Methods: A comprehensive literature search was conducted across major electronic databases, including Scopus, PubMed, ScienceDirect, and Web of Science. Peer-reviewed articles published between 2015 and 2026 were considered for inclusion. Several 3D printing methods can be used to create bioceramic or composite scaffolds for the regeneration of dental, oral, or maxillofacial tissues. Results: Additive manufacturing enables customization of bioceramic scaffolds. This report emphasizes the osteoconductive properties, biodegradability, and compatibility of calcium phosphate, bioactive glass, and calcium silicate ceramics. Conclusions: This review helps to determine how 3D-printed bioceramics can be optimized for dental and maxillofacial applications tailored to specific patients. Full article

Fully polarimetric SAR provides richer scattering information than single-polarisation imaging, but multichannel sparse image formation can be computationally and memory demanding, especially when channels are processed jointly. In our previous work, we introduced Orthogonal Matching Pursuit 2D Fully Polarimetric (OMP2D-FP), a greedy reconstruction algorithm that enforces a shared spatial support across polarimetric channels while exploiting a separable 2D formulation to avoid vectorisation and reduce computational burden and memory footprint relative to vectorised OMP-based formulations. In this paper, we extend its validation to real measurements and further develop its theoretical foundations by recasting the atom-selection step as a detection–estimation problem, thereby defining a cumulative objective function (COF) design space that enables the incorporation of disturbance statistics and prior knowledge into sparse recovery. Experiments on fully polarimetric SAR data of a T-72 tank over a wide range of aspect angles, SNR levels, and measurement percentages show that joint support selection improves reconstruction fidelity and polarimetric information preservation over independent per-channel processing, with particularly clear gains under challenging conditions. Preliminary applications of the COF framework (a whitening COF incorporating polarimetric clutter statistics and a mask-based COF incorporating spatial prior knowledge) yield encouraging results, motivating further systematic investigation of adaptive COF designs. Full article

Spatial intelligence in autonomous driving requires object-level 3D geometry, yet existing monocular mesh reconstruction methods usually operate with a fixed inference path and a single mesh parameterization, which limits their flexibility under heterogeneous resource constraints. To address this issue, we propose DyPRSI, a dynamic-parameterized framework for monocular vehicle 3D reconstruction that provides multiple predefined accuracy–latency operating points within a single model. DyPRSI inserts two early exits into a shared Res2Net–BiFPN trunk and associates each exit with an exit-specific mesh specification, forming a coarse-to-fine reconstruction hierarchy across network depth. To better match the efficiency requirements of shallow branches, DyPRSI adopts lightweight coordinate-classification keypoint decoding for EE1 and EE2, while retaining a heatmap-regression keypoint head in the Main branch to preserve the upper bound of reconstruction accuracy. Experiments on ApolloCar3D show that DyPRSI-Main achieves competitive reconstruction performance, whereas EE1 and EE2 substantially reduce end-to-end inference latency and provide useful alternatives under different resource requirements. Ablation studies further show that the speedup mainly comes from the lightweight branch-specific keypoint heads, while the exit-specific mesh settings help organize stable coarse-to-fine reconstruction behavior across branches. These results indicate that DyPRSI is a practical monocular vehicle reconstruction framework for resource-aware spatial intelligence. Full article

Background/Objectives: Reconstruction of orbital floor fractures remains surgically challenging due to limited intraoperative visibility and complex anatomy. Inaccurate implant placement often leads to persistent complications and the need for a revision surgery. This study evaluated the clinical accuracy and re-operation rates of a preoperative 3D-printed model-assisted technique compared to the conventional intraoperative free-hand mesh bending method. Methods: A comparative ambispective study was conducted on 74 patients with isolated orbital floor fractures. The control group (n = 34, retrospective) underwent reconstruction using intraoperatively formed titanium meshes. In the study group (n = 40, prospective), patient-specific 3D-printed models, created by mirroring the healthy contralateral orbit, were used for preoperative mesh adaptation. Primary outcomes included the rate of revision surgery due to implant malposition, changes in orbital volume, and postoperative diplopia. Results: The 3D model group demonstrated a significantly lower rate of revision surgery compared to the control group. In the retrospective group, 5 patients (15%) required reoperation due to implant malposition, whereas no patients (0%) in the prospective 3D group required secondary intervention (p = 0.017). While both techniques effectively restored orbital volume, the 3D group showed greater volumetric precision with less variance. The mean volume difference in the affected orbit was 3078 ± 2204 mm³ in the control group, compared to 2390 ± 1893 mm³ in the study 3D group. At the 6-month follow-up, persistent diplopia was observed in 12% of the control group compared to only 3% in the study group. Conclusions: The use of in-house 3D-printed models for preoperative mesh forming significantly enhances surgical precision and eliminates the need for revision surgery due to implant malposition. This workflow offers a cost-effective, predictable, and accessible alternative to expensive patient-specific implants (PSIs) or intraoperative navigation systems, improving patient safety and long-term clinical outcomes. Full article

The high-fidelity 3D digitization of small, detailed cultural heritage objects, such as Oracle Bones, presents significant challenges for which existing reconstruction workflows are often inadequate. Methods based on Structure-from-Motion (SfM) often lack the geometric density required to capture fine inscription details, while Light Detection and Ranging and RGB-Depth approaches may introduce high data overhead and unstable color mapping. Recent specialized studies have utilized multi-shading-based techniques to extract such hidden surface textures, yet integrating these results into a cohesive mesh remains difficult. To address these limitations, we propose a digitization framework specifically designed for object-level archaeological artifacts. Our method combines semi-automatic alignment with ICP-based refinement for robust camera pose estimation, reducing misalignment issues associated with feature-only registration. Furthermore, we employ an efficient mesh-based representation with vertex-level coloring, enabling detailed geometry and consistent texturing while maintaining compact storage requirements. Our contributions include: (1) a high-quality mesh reconstruction framework that preserves fine inscription geometry; (2) a hybrid camera pose estimation strategy that improves alignment robustness; and (3) an integrated hardware-assisted workflow tailored for digitizing small archaeological artifacts under controlled acquisition conditions. Experimental results on physical Oracle Bone artifacts demonstrate that the proposed method achieves a mean geometric reconstruction error of approximately 0.075 mm with a Hausdorff distance of 1 mm. These results demonstrate the effectiveness of the proposed workflow for digitization of oracle bone artifacts. Full article

The main objective of the present study is to reveal the palette of pigments and the other specific constituent materials as well as the techniques used by the Roman artists to create the mural paintings discovered in the ancient city of Tomis, the modern-day Constanţa, Romania’s largest seaport and a major tourist hub on the Black Sea. This paper is an archeometric study based on the physical, chemical and biological analyses of the archeological Roman mural painting fragments from the ancient city of Tomis dating from the 5th to 6th century A.D. and to our knowledge is among the very few research studies carried out so far on the ancient Roman wall painting discovered in Romania. The methods of scientific investigation employed directly on the archeological fragments, on samples taken from the fragments and on the cross-sections prepared from the samples were: optical microscopy (OM), digital microscopy, X-ray fluorescence spectrometry (XRF) and attenuated total reflectance Fourier-transform infrared spectroscopy (ATR-FTIR). Examination and analysis of the archeological mural fragments revealed that the painted fragments consist of ground support and successive layers of color displaying specific characteristics of the artistic technique, such as imitation of marble cladding or meticulous smoothing of the surface to achieve a glossy and compact finish. It was also found that fragments exhibit subtle variations in different colors, identified in general terms, showing seven color tones: cinnabar red, red-violet, red ochre, yellow ochre, white, gray-blue, gray-black and black. The physical–chemical and biological analyses carried out provide the diagnosis and theoretical basis for choosing an appropriate conservation methodology and the correct restoration treatment of the discovered mural painting, with a view to its museum display through exhibition and virtual reconstruction and scientific use by the setting up of a useful database for researchers or specialists in museums on Roman archeology and art. Full article

Background/Objectives: Robot-assisted partial nephrectomy (RAPN) remains a technically demanding procedure, associated with a non-negligible risk of perioperative complications. This study aimed to assess the impact of preoperative planning and intraoperative navigation using patient-specific three-dimensional (3D) virtual model reconstructions on perioperative outcomes of RAPN. Methods: We analyzed 307 patients who underwent RAPN for renal tumors at a tertiary center between 2021 and 2024. Starting in 2023, 3D modeling (Medics3D) was integrated for selected cases (n = 69) and compared to a 2D-imaging control group (n = 238). The primary outcome was trifecta achievement, defined as the simultaneous presence of negative surgical margins, ≥90% preservation of preoperative eGFR at discharge, and absence of perioperative complications. Clamping strategies were categorized as on-clamp, selective/super-selective, or off-clamp. Mann–Whitney and Chi-squared tests compared the groups; multivariable logistic regression identified independent predictors of trifecta achievement. Results: Baseline characteristics were balanced between the 3D and control groups: median age (62 vs. 61 years, p = 0.5), BMI (28 vs. 26, p = 0.3), and eGFR (85 vs. 86 mL/min/1.73 m², p = 0.5). Median tumor size was 4.2 vs. 4.0 cm (p = 0.4), and RENAL complexity was comparable (p = 0.12). Selective or super-selective clamping was significantly more frequent in the 3D group (32% vs. 15%; p < 0.01). While WIT (17.5 vs. 18.5 min, p = 0.09) and complication rates (26% vs. 29%, p = 0.7) were similar, the 3D group showed a significantly lower rate of positive surgical margins (5% vs. 15%; p = 0.030). Trifecta achievement was significantly higher in the 3D group (51% vs. 32%; p = 0.004). On multivariable analysis, 3D modeling remained an independent predictor of trifecta achievement (OR 2.1, 95% CI 1.17–3.70; p = 0.013). Conclusions: The use of patient-specific 3D kidney reconstructions was associated with improved perioperative outcomes in patients undergoing RAPN. These findings support the integration of 3D modeling into routine surgical workflows to enhance operative precision and optimize patient outcomes. Full article

Digital fabrication technologies increasingly enable designers and researchers to reinterpret historical design languages through contemporary production methods. Within this context, desktop 3D printing offers an accessible yet constrained medium for translating stylistically rich interior design objects into tangible form. This study examines how distinct twentieth-century interior design movements—Art Deco, Bauhaus, and Mid-century Modern—are mediated through desktop additive manufacturing, focusing on the preservation of formal identity rather than manufacturing performance. Representative interior objects were digitally reconstructed from archival and reference material and fabricated under standardized desktop 3D printing conditions. The investigation adopts a style-oriented evaluation framework that examines silhouette continuity, characteristic geometric features, ornamental legibility, and structural–stylistic coherence. To support comparative interpretation, a Style Preservation Index (SPI) is introduced as a structured design evaluation tool that makes stylistic assessment explicit and repeatable without reducing it to purely geometric metrics. The results demonstrate that stylistic legibility is preserved to differing degrees depending on the formal vocabulary of each design movement, with minimal and geometrically rational styles exhibiting higher compatibility with layer-based fabrication than ornamentally dense or materially expressive designs. Rather than framing these differences as technological limitations, the study interprets them as insights into how design languages interact with fabrication constraints. By positioning desktop additive manufacturing as a medium of design translation rather than replication, this work contributes a reproducible framework for design research, heritage interpretation, and education, offering a structured approach for exploring how historical styles can be re-engaged through contemporary digital fabrication. Full article

Accurate and spatially resolved yield estimation is a critical requirement for precision agriculture and orchard management. This paper presents a geometrically consistent, orchard-scale apple yield estimation framework that integrates GNSS–visual-inertial odometry (VIO) fusion, deep learning-based object detection, multi-frame tracking, three-dimensional triangulation, and incremental factor-graph optimization. Camera poses are obtained using ZED GNSS–VIO fusion and subsequently refined using an iSAM2-based nonlinear smoothing approach that incorporates strong relative-motion constraints and soft global ENU (East-North-Up) translation priors. Apples are detected using a YOLO-based model and associated across frames via CoTracker3, enabling robust multi-view landmark reconstruction. Reprojection factors and landmark priors are incorporated into a unified nonlinear factor graph to jointly optimize camera trajectories and 3D apple positions. The reconstructed apples are spatially aggregated into a grid-based mass map, where individual fruit volumes are estimated assuming spherical geometry and converted to mass using density models. The resulting ENU-referenced yield plot provides a structured representation of orchard production variability. Experimental results demonstrate significant reductions in reprojection error after optimization and improved global consistency of the trajectory, leading to stable and spatially coherent 3D reconstructions. The proposed pipeline bridges perception, geometry, and optimization, providing a scalable solution for orchard-scale yield mapping and decision support in precision agriculture. Full article