Search Results (472)

Traditional unmanned aerial vehicle (UAV) path planning methods for image-based 3D reconstruction often rely solely on geometric information from initial models, resulting in redundant data acquisition in non-architectural areas. This paper proposes a UAV path planning method via semantic segmentation of 3D reality mesh models to enhance efficiency and accuracy in complex scenarios. The scene is segmented into buildings, vegetation, ground, and water bodies. Lightweight polygonal surfaces are extracted for buildings, while planar segments in non-building regions are fitted and projected into simplified polygonal patches. These photography targets are further decomposed into point, line, and surface primitives. A multi-resolution image acquisition strategy is adopted, featuring high-resolution coverage for buildings and rapid scanning for non-building areas. To ensure flight safety, a Digital Surface Model (DSM)-based shell model is utilized for obstacle avoidance, and sky-view-based Real-Time Kinematic (RTK) signal evaluation is applied to guide viewpoint optimization. Finally, a complete weighted graph is constructed, and ant colony optimization is employed to generate a low-energy-cost flight path. Experimental results demonstrate that, compared with traditional oblique photogrammetry, the proposed method achieves higher reconstruction quality. Compared with the commercial software Metashape, it reduces the number of images by 30.5% and energy consumption by 37.7%, while significantly improving reconstruction results in both architectural and non-architectural areas. Full article

Reversible data hiding in encrypted point clouds presents unique challenges due to their unstructured geometry, absence of mesh connectivity, and high sensitivity to spatial perturbations. In this paper, we propose an efficient and secure reversible data hiding framework for encrypted point clouds, incorporating KD tree-based path planning, adaptive multi-MSB prediction, and a dual-model design. To establish a consistent spatial traversal order, a Hamiltonian path is constructed using a KD tree-accelerated nearest-neighbor algorithm. Guided by this path, a prediction-driven embedding strategy dynamically adjusts the number of most significant bits (MSBs) embedded per point, balancing capacity and reversibility while generating a label map that reflects local predictability. The label map is then compressed using Huffman coding to reduce the auxiliary overhead. For enhanced security and lossless recovery, the encrypted point cloud is divided into two complementary shares through a lightweight XOR-based (2, 2) secret sharing scheme. The Huffman tree and compressed label map are distributed across both encrypted shares, ensuring that neither share alone can reveal the original point cloud or the embedded message. Experimental evaluations on diverse 3D models demonstrate that the proposed method achieves near-optimal embedding rates, perfect reconstruction of the original model, and significant obfuscation of the geometric structure. These results confirm the practicality and robustness of the proposed framework for scenarios involving secure 3D point cloud transmission, storage, and sharing. Full article

Background/Objectives: In the search for optimal meshes and matrices in breast surgery, poly-4-hydroxybutyrate (P4HB) has emerged as a promising alternative. This review evaluates the clinical application of P4HB scaffolds, focusing on complication rates and surgical outcomes. Methods: A systematic search was conducted using PubMed and ScienceDirect. Clinical studies assessing perioperative outcomes and complications associated with P4HB scaffolds in breast surgery were included. Results were stratified into aesthetic and reconstructive surgery categories. Meta-analysis was implemented to assess the rate of complications and satisfaction. Results: This systematic review included 13 studies evaluating the use of P4HB scaffold in breast reconstruction (636 cases) and aesthetic breast surgery (462 patients). Breast reconstruction studies were all retrospective, mainly reporting two-stage, prepectoral, immediate reconstructions. Aesthetic studies included both prospective and retrospective designs, with varied implant planes and incision patterns. P4HB use was associated with high satisfaction (95.5%) and favorable outcomes, including lower odds of wound complications (log-OR = −1.135, p = 0.003). Complication rates were low across both surgical categories. P4HB scaffold showed promise in supporting implant-based procedures and maintaining breast shape over time, with minimal increase in surgical time and stable anthropometric measurements. Conclusions: The use of P4HB scaffold in breast reconstruction and aesthetic surgery shows promising results, notably in reducing wound-related complications. Breast reconstruction studies report low complication rates and favorable patient-reported outcomes. In aesthetic procedures, P4HB contributes to improved long-term breast shape and high satisfaction. Despite encouraging findings, further research is necessary to validate long-term efficacy and refine surgical approaches. Full article

Background: Pelvic resections represent some of the most challenging procedures in orthopedic oncology, often necessitating the sacrifice of large bone segments and, subsequently, the loss of nearby soft tissues. Our study aims to evaluate the impact of surgical resections of pelvic bone tumors on the performance of the pelvic floor and digestive, urinary, and genital systems. Methods: We evaluated all malignant or locally aggressive pelvic bone tumors treated with bone resection in our institution between January 2017 and January 2024. The reconstructive approaches were recorded. Pre- and post-operative MRI and CT scans were used to evaluate the grade of pelvic prolapse. The prolapse of the pelvic floor was assessed with the M-line, the H-line, and the anorectal angle. Hydronephrosis was also evaluated. Urinary and fecal incontinence were evaluated with the Pelvic Floor Impact Questionnaire (PFIQ7). Results: Thirty cases were included in our study. Nine cases were treated with custom-made prostheses, five had ice-cone prostheses, two massive allografts, and one composite allograft-prosthesis. The others had no bone reconstruction. Meshes were used to reconstruct the pelvic floor in 9 cases. Patients with discontinuity of the pelvic ring had a significantly higher grade of pelvic prolapse (M-line) and worse PFIQ7 scores. Conclusions: The resection of pelvic bone tumors represents one of the main challenges in orthopedic oncology. While planning surgical demolition and performing the subsequent reconstruction, surgeons should also consider the impact of the surgical treatment on the pelvic floor and surrounding organs. Intra-operative reconstructions and post-operative rehabilitation are advisable. Full article

Intraosseous hemangiomas (IH) are rare intrabony lesions that represent less than 1% of intraosseous tumors. IH are mostly seen in the axial skeleton and skull. Most commonly, the frontal bone, zygomatic, sphenoid, maxilla, ethmoid, and lacrimal bone can manifest IH. Currently, IH is classified as a developmental condition of endothelial origin. According to WHO, the five histological types of IH are cavernous, capillary, epithelioid, histiocytoid, and sclerosing. IH of the zygoma is an extremely rare condition with female predominance. A systematic review recently estimated that there were 78 cases published in the literature until 2023. The lesion is usually asymptomatic and presents with a gradually deteriorating deformity of the malar area, and the patient might be able to recall a history of trauma. Numbness due to involvement of the infraorbital nerve might also be present; however, atypical skin and bone sensations might also occur. Other symptoms include painful swelling, bone asymmetry, skin irritation, sinus pressure, paresthesia, diplopia, enophthalmos, or atypical neuralgia. A bony lesion with a trabecular pattern in a radiating formation (sunburst pattern) or a multilocal lytic lesion pattern created by the multiple cavernous spaces (honeycomb pattern) is commonly observed during radiologic evaluation. We present a rare case of IH of the zygoma in a 65-year-old generally healthy woman. A cyst-like bone tumor was revealed from the CT scan, which made preoperative biopsy of the lesion problematic. A careful radiological diagnostic differentiation of the lesion should always be conducted in such cases to outline a safe surgical plan and possible alternatives if needed. The patient underwent total tumor resection in the operating room, and the defect was reconstructed with the use of a titanium mesh and a synthetic hydroxyapatite bone graft based on a 3D surgical guide printed model. Full article

The accurate reconstruction of piecewise continuous functions on meshes is challenging due to potential spurious oscillations—namely the Gibbs phenomenon—especially for high-order methods. This paper introduces the Robust Discontinuity Indicators (RDI) method, a novel technique for constructing discontinuity indicators. These indicators can effectively identify both $C^0$ and $C^1$ discontinuities in a single pass using a new comprehensive theoretical analysis combined with cell-based overshoot–undershoot indicators and node-based oscillation indicators. In addition to detecting discontinuities, these indicator values can also facilitate the construction of adaptive weighting schemes to mitigate the Gibbs phenomenon. Due to its flexibility, RDI can accommodate complex geometries and applies to nonuniform unstructured meshes and general surfaces, broadening its utility. Through experiments, we show that RDI can accurately capture discontinuities while producing fewer false positives than two-pass methods. By providing a more rigorous method for discontinuity detection, RDI has the potential to offer significant improvements in computational simulations and data remapping. Full article

Background/Objectives: This case study presents the first documented use of a low-cost, simulated, patient-specific three-dimensional (3D) printed model to support presurgical planning for an infant with Apert syndrome in a resource-limited setting. The primary objectives are to (1) demonstrate the value of 3D printing as a simulation tool for preoperative planning in low-resource environments and (2) identify opportunities for future AI-enhanced simulation models in craniofacial surgical planning. Methods: High-resolution CT data were segmented using InVesalius 3, with mesh refinement performed in ANSYS SpaceClaim (version 2021). The cranial model was fabricated using fused deposition modeling (FDM) on a Creality Ender-3 printer with Acrylonitrile Butadiene Styrene (ABS) filament. Results: The resulting 3D-printed simulated model enabled the surgical team to assess cranial anatomy, simulate incision placement, and rehearse osteotomies. These steps contributed to a reduction in operative time and fewer complications during surgery. Conclusions: This case demonstrates the value of accessible 3D printing as a simulation tool in surgical planning within low-resource settings. Building on this success, the study highlights potential points for AI integration, such as automated image segmentation and model reconstruction, to increase efficiency and scalability in future 3D-printed simulation models. Full article

This research introduces an automated 3D virtual reconstruction system tailored for architectural heritage (AH) applications, contributing to the ongoing paradigm shift from traditional CAD-based workflows to artificial intelligence-driven methodologies. It reviews recent advancements in machine learning and deep learning—particularly neural radiance fields (NeRFs) and its successor, Gaussian splatting (GS)—as state-of-the-art techniques in the domain. The study advocates for replacing point cloud data in heritage building information modeling workflows with image-based inputs, proposing a novel “photo-to-BIM” pipeline. A proof-of-concept system is presented, capable of processing photographs or video footage of ancient ruins—specifically, Romanesque–Mudéjar churches—to automatically generate 3D mesh reconstructions. The system’s performance is assessed using both objective metrics and subjective evaluations of mesh quality. The results confirm the feasibility and promise of image-based reconstruction as a viable alternative to conventional methods. The study successfully developed a system for automated 3D mesh reconstruction of AH from images. It applied GS and Mip-splatting for NeRFs, proving superior in noise reduction for subsequent mesh extraction via surface-aligned Gaussian splatting for efficient 3D mesh reconstruction. This photo-to-mesh pipeline signifies a viable step towards HBIM. Full article

This paper presents a neural network model, PINN-AeroFlow-U, for reconstructing full-field aerodynamic quantities around three-dimensional compressor blades, including regions near the wall. This model is based on structured CFD training data and physics-informed loss functions and is proposed for direct 3D compressor flow prediction. It maps flow data from the physical domain to a uniform computational domain and employs a U-Net-based neural network capable of capturing the sharp local transitions induced by fluid acceleration near the blade leading edge, as well as learning flow features associated with internal boundaries (e.g., the wall boundary). The inputs to PINN-AeroFlow-U are the flow-field coordinate data from high-fidelity multi-geometry blade solutions, the 3D blade geometry, and the first-order metric coefficients obtained via mesh transformation. Its outputs include the pressure field, temperature field, and velocity vector field within the blade passage. To enhance physical interpretability, the network’s loss function incorporates both the Euler equations and gradient constraints. PINN-AeroFlow-U achieves prediction errors of 1.063% for the pressure field and 2.02% for the velocity field, demonstrating high accuracy. Full article

Three-dimensional imaging technologies are increasingly used in breast reconstructive and plastic surgery due to their potential for efficient and accurate preoperative assessment and planning. This study systematically evaluates the accuracy and consistency of six commercially available 3D scanning applications (apps)—Structure Sensor, 3D Scanner App, Heges, Polycam, SureScan, and Kiri—in reconstructing the female torso. To avoid variability introduced by human subjects, a silicone breast mannequin model was scanned, with fiducial markers placed at known anatomical landmarks. Manual distance measurements were obtained using calipers by two independent evaluators and compared to digital measurements extracted from 3D reconstructions in Blender software. Each scan was repeated six times per application to ensure reliability. SureScan demonstrated the lowest mean error (2.9 mm), followed by Structure Sensor (3.0 mm), Heges (3.6 mm), 3D Scanner App (4.4 mm), Kiri (5.0 mm), and Polycam (21.4 mm), which showed the highest error and variability. Even the app using an external depth sensor (Structure Sensor) showed no statistically significant accuracy advantage over those using only the iPad’s built-in camera (except for Polycam), underscoring that software is the primary driver of performance, not hardware (alone). This work provides practical insights for selecting mobile 3D scanning tools in clinical workflows and highlights key limitations, such as scaling errors and alignment artifacts. Future work should include patient-based validation and explore deep learning to enhance reconstruction quality. Ultimately, this study lays the foundation for more accessible and cost-effective 3D imaging in surgical practice, showing that smartphone-based tools can produce clinically useful scans. Full article

Creating high-fidelity digital twins (DTs) for Industry 4.0 applications, it is fundamentally reliant on the accurate 3D modeling of physical assets, a task complicated by the inherent imperfections of real-world point cloud data. This paper addresses the challenge of reconstructing accurate, watertight, and topologically sound 3D meshes from sparse, noisy, and incomplete point clouds acquired in complex industrial environments. We introduce a robust two-stage completion-to-reconstruction framework, C2R3D-Net, that systematically tackles this problem. The methodology first employs a pretrained, self-supervised point cloud completion network to infer a dense and structurally coherent geometric representation from degraded inputs. Subsequently, a novel adaptive surface reconstruction network generates the final high-fidelity mesh. This network features a hybrid encoder (FKAConv-LSA-DC), which integrates fixed-kernel and deformable convolutions with local self-attention to robustly capture both coarse geometry and fine details, and a boundary-aware multi-head interpolation decoder, which explicitly models sharp edges and thin structures to preserve geometric fidelity. Comprehensive experiments on the large-scale synthetic ShapeNet benchmark demonstrate state-of-the-art performance across all standard metrics. Crucially, we validate the framework’s strong zero-shot generalization capability by deploying the model—trained exclusively on synthetic data—to reconstruct complex assets from a custom-collected industrial dataset without any additional fine-tuning. The results confirm the method’s suitability as a robust and scalable approach for 3D asset modeling, a critical enabling step for creating high-fidelity DTs in demanding, unseen industrial settings. Full article

Background and Objectives: Orbital floor fractures are challenging to treat, due to the complex orbital anatomy and limited surgical access. Emerging technologies—such as virtual surgical planning (VSP), 3D printing, patient-specific implants (PSIs), and intraoperative navigation—offer promising advancements to improve the surgical precision and clinical outcomes. This review systematically evaluates and synthesizes current technological modalities with respect to their accuracy, operative duration, cost-effectiveness, and postoperative functional outcomes. Materials and Methods: A systematic review was conducted according to the PRISMA 2020 guidelines. The PubMed, Scopus, and PRIMO databases were searched for clinical studies published between 2019 and September 2024. Out of 229 articles identified, 9 met the inclusion criteria and were analyzed using the PICO framework. Results: VSP and 3D printing enhanced diagnostics and presurgical planning, offering improved accuracy and reduced planning time. Pre-bent PSIs shaped on 3D models showed superior accuracy, lower operative times, and better cost efficiency compared to intraoperative mesh shaping. Custom-designed PSIs offered high precision and clinical benefit but required a longer production time. Intraoperative navigation improved implant positioning and reduced the complication rates, though a detailed cost analysis remains limited. Conclusions: VSP, 3D printing, and intraoperative navigation significantly improve surgical planning and outcomes in orbital floor reconstruction. Pre-bent PSIs provide a time- and cost-effective solution with strong clinical performance. While customized PSIs offer accuracy, they are less practical in time-sensitive settings. Navigation systems are promising tools that enhance outcomes and may serve as an alternative to custom implants when time or resources are limited. Full article

Under the context of the dual-carbon policy, reducing energy consumption and emissions in automobiles has garnered significant attention, with automotive lightweighting being particularly important. This paper focuses on the lightweight design of automotive subframes, aiming to minimize weight while meeting performance requirements. Research has revealed that the original subframe allows further room for lightweighting and performance optimization. A topology optimization model was established using the Solid Isotropic Material with Penalization (SIMP) method and solved using the Method of Moving Asymptotes (MMA) algorithm. Integration of the SIMP method was achieved on the Abaqus-Matlab (2022x) platform via Python (3.11.0) and Matlab (R2022a) coding, forming an effective optimization framework. The optimization results provided clear load transfer paths, offering a theoretical basis for geometric model conversion. The subframe model was subsequently reconstructed in CATIA. Material redundancy was identified in the reconstructed subframe model, prompting secondary optimization. Multi-objective size optimization was conducted in OptiStruct, reducing the subframe’s mass from 33.73 kg to 17.84 kg, achieving a 47.1% weight reduction. Static stiffness and modal analyses performed in HyperMesh confirmed that results met all relevant standards. Modal testing revealed a minimal deviation of only −2.7% from the simulation results, validating the feasibility and reliability of the optimized design. This research demonstrates that combining topology optimization with size optimization can significantly reduce weight and enhance subframe performance, providing valuable support for future automotive component design. Full article

High-pitched noise generated by dental air turbine handpieces (ATHs) causes discomfort and anxiety, discouraging dental visits. Understanding the time-dependent noise generation mechanism associated with compressed airflow in ATHs is crucial for effective noise reduction. However, the direct investigation of airflow dynamics within ATHs is challenging. The transient-state modeling of computational fluid dynamics (CFD) simulations remains unexplored owing to the complexities of high rotational speeds and air compressibility. This study develops a novel CFD framework for transient (time-dependent) modeling under high-speed rotational conditions. Simulations were performed using a three-dimensional model reconstructed from a commercial ATH. Simulations were conducted at 320,000 rpm using a novel framework that combines the immersed boundary and building cube methods. A fine 0.025 mm mesh spacing near the ATH, combined with supercomputing resources, enabled the simulation of hundreds of millions of cells. The simulation results were validated using experimental noise measurements. The CFD simulation revealed transient airflow and aeroacoustic behavior inside and around the ATH that closely matched the prominent frequency peaks from the experimental data. This study is the first to simulate the transient airflow of ATHs. The proposed CFD model can accurately predict aeroacoustics, contributing to the future development of quieter and more efficient dental devices. Full article

Hundreds of Japanese features of war (field positions, tunnels, and fortifications) were constructed in Hong Kong during World War II. However, most of them were poorly documented and were left unknown but still in relatively good condition because of their durable design, workmanship, and remoteness. These features of war form parts of Hong Kong’s brutal history. Conservation, at least in digital form, is worth considering. With the authors coming from multidisciplinary and varied backgrounds, this paper aims to explore these features using a scientific workflow. First, we reviewed the surviving archival sources of the Imperial Japanese Army and Navy. Second, airborne LiDAR data were used to form territory digital terrain models (DTM) based on the Red Relief Image Map (RRIM) for identifying suspected locations. Third, field expeditions of searching for features of war were conducted through guidance of Global Navigation Satellite System—Real-Time Kinetics (GNSS-RTK). Fourth, the found features were 3D-laser scanned to generate mesh models as a digital archive and validate the findings of DTM-RRIM. This study represents a reverse-engineering effort to reconstruct the planned Japanese defense tactics of guerilla fight and Kamikaze grottos that were never used in Hong Kong. Full article