3D Model Reconstruction Research Articles

Innovative Use of 3D Reconstruction Models of the Pulmonary Artery for Preoperative Planning

Innovative Use of 3D Reconstruction Models of the Pulmonary Artery for Preoperative Planning

Enhanced Multi-Scale Attention-Driven 3D Human Reconstruction from Single Image

Due to the inherent limitations of a single viewpoint, reconstructing 3D human meshes from a single image has long been a challenging task. While deep learning networks enable us to approximate the shape of unseen sides, capturing the texture details of the non-visible side remains difficult with just one image. Traditional methods utilize Generative Adversarial Networks (GANs) to predict the normal maps of the non-visible side, thereby inferring detailed textures and wrinkles on the model’s surface. However, we have identified challenges with existing normal prediction networks when dealing with complex scenes, such as a lack of focus on local features and insufficient modeling of spatial relationships.To address these challenges, we introduce EMAR—Enhanced Multi-scale Attention-Driven Single-Image 3D Human Reconstruction. This approach incorporates a novel Enhanced Multi-Scale Attention (EMSA) mechanism, which excels at capturing intricate features and global relationships in complex scenes. EMSA surpasses traditional single-scale attention mechanisms by adaptively adjusting the weights between features, enabling the network to more effectively leverage information across various scales. Furthermore, we have improved the feature fusion method to better integrate representations from different scales. This enhanced feature fusion allows the network to more comprehensively understand both fine details and global structures within the image. Finally, we have designed a hybrid loss function tailored to the introduced attention mechanism and feature fusion method, optimizing the network’s training process and enhancing the quality of reconstruction results. Our network demonstrates significant improvements in performance for 3D human model reconstruction. Experimental results show that our method exhibits greater robustness to challenging poses compared to traditional single-scale approaches.

Enhancing computational fluid dynamics simulation for coronary artery disease analysis through spatial iterative modeling

Abstract Background Endothelial shear stress (ESS) critically influences the pathogenesis of coronary artery disease (CAD) by modulating the formation and progression of atherosclerotic lesions. Despite the use of 3D modeling in computational fluid dynamics (CFD) simulations to calculate ESS, the precision of these models significantly depends on the method of reconstruction. Spatial Iterative Method (SIM) extends the iterative approach in computer science to spatial data, using recurring algorithms to refine 3D models and analyses. Iterative approach executes a sequence of instructions repeatedly until a certain condition is met, enabling progressive problem-solving. Purpose In this study, we investigated the application of the SIM to estimate ESS in coronary arteries and explored Atherosclerotic Plaque Areas (APA) in patients with CAD. Methods We analyzed fifteen patients (9 males, mean age of 63 ± 10.56 years) with atherosclerotic plaque on coronary arteries. The eighteen images captured from an anterior viewpoint in 10-degree increments of the coronary artery obtained from Coronary Computed Tomography Angiography at the best diastolic phase for each patient were used. We implemented the SIM for 3D model reconstruction, facilitating ninety comparisons across all iterations to evaluate ESS estimations accurately. (Figure 1.) Results In our study, we evaluated ESS contours from CFD simulations for a total of 282 APA (200 calcified and 82 soft plaques) across ninety spatial iterations. We categorized ESS into three groups: low (162 APA cases with ESS of less than 1 Pascal (Pa)), medium (67 APA cases with ESS between 1 and 2 Pa), and high (53 APA cases with ESS greater than 2 Pa). There was a significant association between ESS categories and plaque types (p < 0.0001, Figure 2.). The calcified plaques were formed in areas with low ESS (%80 of calcified plaques) and soft plaques in areas with medium and high ESS (%97 percent of soft plaques). Conclusion The significant association between ESS categories and plaque types shows the potential of SIM in hemodynamic analysis. This finding highlights the potential of SIM in enhancing the understanding of hemodynamic influences on plaque formation, offering a promising direction for future cardiovascular research.Figure 1.Sample Iteration of the SIMFigure 2.Heatmap of ESS vs. APA

High-precision 3D Modeling of Construction Waste Pile Bodies by Integrating Multi-source Data

Abstract. The instability of construction waste pile bodies, as an increasingly concerning disaster, poses significant risks to the safety of people's lives and property. Currently, there is limited research on high-precision 3D modeling techniques for construction waste pile bodies, which significantly hinders the accuracy and reliability of early detection and risk assessment of pile body instability. Therefore, constructing high-precision 3D models of construction waste pile bodies using multi-source data plays a crucial role in improving the accuracy and timeliness of early warning systems for pile body instability, offering significant theoretical research and practical application value. Building 3D models of construction waste pile bodies solely based on unmanned aerial vehicle (UAV) oblique photography data faces challenges, such as various degrees of data voids and insufficient model completeness, and few methods currently address the construction of 3D models of construction waste pile bodies through the fusion of multi-source data. This paper attempts to use the Iterative Closest Point (ICP) algorithm, combining SLAM laser point cloud data with UAV oblique photography data to fill data voids, and utilizes the fused point cloud to reconstruct the triangulation network, achieving the transformation from 3D point cloud models to 3D surface models. On the basis of texture mapping, a high-precision 3D model of the construction waste pile body is constructed. The research results show that the 3D model of the construction waste pile body, integrated with multi-source data, has a planar error of 0.0187m and an elevation error of 0.0368m, meeting the corresponding model accuracy requirements. It can more realistically restore the fine 3D features of the construction waste pile body, effectively compensating for the shortcomings of single-source data in 3D modeling of construction waste pile bodies, providing a new method for the 3D model reconstruction of construction waste pile bodies, and offering effective data support for construction waste research.

DSM Reconstruction from Uncalibrated Multi-View Satellite Stereo Images by RPC Estimation and Integration

In this paper, we propose a 3D Digital Surface Model (DSM) reconstruction method from uncalibrated Multi-view Satellite Stereo (MVSS) images, where Rational Polynomial Coefficient (RPC) sensor parameters are not available. While recent investigations have introduced several techniques to reconstruct high-precision and high-density DSMs from MVSS images, they inherently depend on the use of geo-corrected RPC sensor parameters. However, RPC parameters from satellite sensors are subject to being erroneous due to inaccurate sensor data. In addition, due to the increasing data availability from the internet, uncalibrated satellite images can be easily obtained without RPC parameters. This study proposes a novel method to reconstruct a 3D DSM from uncalibrated MVSS images by estimating and integrating RPC parameters. To do this, we first employ a structure from motion (SfM) and 3D homography-based geo-referencing method to reconstruct an initial DSM. Second, we sample 3D points from the initial DSM as references and reproject them to the 2D image space to determine 3D–2D correspondences. Using the correspondences, we directly calculate all RPC parameters. To overcome the memory shortage problem while running the large size of satellite images, we also propose an RPC integration method. Image space is partitioned to multiple tiles, and RPC estimation is performed independently in each tile. Then, all tiles’ RPCs are integrated into the final RPC to represent the geometry of the whole image space. Finally, the integrated RPC is used to run a true MVSS pipeline to obtain the 3D DSM. The experimental results show that the proposed method can achieve 1.455 m Mean Absolute Error (MAE) in the height map reconstruction from multi-view satellite benchmark datasets. We also show that the proposed method can be used to reconstruct a geo-referenced 3D DSM from uncalibrated and freely available Google Earth imagery.

Reconstruction of cancer marker analysis with holistic anatomical precision implicates heterogeneity development during breast tumor progression

Biomarkers are not only of significant importance for cancer diagnosis and selection of treatment plans but also recently increasingly used for the evaluation of malignancy development and tumor heterogeneity. Large-size tumors from clinical patients can be unique and valuable sources for the study of cancer progression, particularly to the extent of intratumoral heterogeneity. In the present study, we obtained a series of post-surgery puncture samples from a breast cancer patient with a 4 × 3.5 × 2 cm tumor in its original size. Immunohistochemistry for Ki-67, COX-2, and CA IX was performed and the expression levels within the breast cancer tumor mass were evaluated in the reconstructed 3D models. To further evaluate the intratumoral heterogeneity, we performed high throughput whole transcriptome sequencing of 12 samples from different spatial positions within the tumor tissue. Comparing the reconstructed 3D distribution of biomarkers with projected tumor growth models, asymmetric and heterogeneous expansion of tumor mass was found to be possibly influenced by factors such as blood supply, inflammation and/or hypoxia stimulations, as suggested from the correlation between the results of Ki-67 and CA IX or COX-2 staining. Furthermore, high-throughput RNA sequencing data provided additional information for profiling the intratumoral heterogeneity and expanded the understanding of cancer progression. Digital technology for medical imaging once properly integrated with molecular pathology examinations will become particularly helpful in dissecting out in-depth information for precision medicine. We prospect that this approach, facilitated by rapidly advancing artificial intelligence, could provide new insights for clinical decision-making in the future. Strategies for the continuous development from the present study for better performance and application were discussed.

Application of 3D Printing to Design and Manufacture Pancreatic Duct Stent and Animal Experiments.

Objective: Postoperative pancreatic fistula (POPF) is a common and challenging complication following pancreaticoduodenectomy (PD), occurring in 2% to 46% of cases. Despite various pancreaticojejunostomy techniques, an effective method to prevent POPF has not been established. This study aimed to develop and evaluate a novel 3D-printed biodegradable pancreatic duct stent to simplify the surgical process of pancreaticojejunostomy, reduce anastomotic complexity, and minimize postoperative complications. Methods: Data from 32 patients undergoing total laparoscopic pancreaticoduodenectomy were utilized. Preoperative CT scans were transformed into 3D reconstructions to guide the design and printing of customized stents using polylactic acid (PLA). The stents were assessed for mechanical integrity, surface texture, and thermal stability. Animal experiments were conducted on 16 mini pigs, with the experimental group receiving the novel stent and the control group receiving traditional silicone stents. Results: The 3D-printed stents demonstrated accurate dimensional replication and mechanical reliability. In the animal experiments, the experimental group showed no significant difference in postoperative complications compared to the control group. At 4 weeks post-surgery, CT scans revealed well-healed anastomoses in both groups, with no significant inflammation or other complications. Histological examination and 3D reconstruction models confirmed good healing and device positioning in the experimental group. Conclusion: The 3D-printed biodegradable pancreatic duct stent offers a promising solution for pancreaticojejunostomy, with comparable safety and efficacy to traditional methods. Further research is needed to validate its clinical application.

Research on the multitarget 3D trajectory tracking method of Thalassodes immissaria in the thermal infrared region based on YOLOX-GMM and SORT-Pest.

Studying the behavioral responses and movement trajectories of insects under different stimuli is crucial for developing more effective biological control measures. Therefore, accurately obtaining the movement trajectories and behavioral parameters of insects in three-dimensional space is essential. This study used the litchi pest Thalassodes immissaria as the research object. A special binocular vision observation system was designed for nighttime movement. A thermal infrared camera was used for video recording of T. immissaria in a lightless environment. Moreover, a multi-object tracking method based on the YOLOX-GMM and SORT-Pest algorithms was proposed for tracking T. immissaria in thermal infrared images. By obtaining the central coordinates of the two T. immissaria in the video, target matching and 3D trajectory reconstruction in the parallel binocular system were achieved. Error analysis of the T. immissaria detection and tracking model, as well as the 3D reconstruction model, showed that the average accuracy of T. immissaria detection reached 89.6%, tracking accuracy was 96.9%, and the average error of the reconstructed 3D spatial coordinates was 15 mm. These results indicate that the method can accurately obtain the 3D trajectory and motion parameters of T. immissaria. Such data can greatly contribute to researchers' comprehensive understanding of insect behavioral patterns and habits, providing important support for more targeted control strategies.

Parametric indoor 3D reconstruction based on binary image feature point extraction

Abstract In recent years, with the rapid advancement of laser scanning technology, indoor 3D modeling technology has significantly developed, becoming a research hotspot in the field of 3D reconstruction. Addressing the issues of high complexity in decoding point cloud surfaces in traditional indoor reconstruction, this paper proposes an indoor 3D reconstruction method based on binary image feature point extraction. The method utilizes point cloud data for semantic segmentation of the indoor environment to accurately extract the ceiling area, which is then projected onto the XOY plane and converted into a binary image, thereby transforming the 3D problem into a 2D problem. Subsequently, key feature points are identified through feature point detection technology, and systematic model construction is carried out based on the sequence of these feature points and their associated parameters. This method effectively simplifies the complexity of traditional 3D modeling, achieving accurate and parametric indoor 3D reconstruction.

Indoor scene reconstruction from LiDAR point cloud based on roof extraction

Indoor scene reconstruction from LiDAR point clouds is crucial for indoor navigation, scan-to-BIM, and virtual reality. However, occlusions caused by furniture and clutter make this task challenging. This paper introduces a novel framework for reconstructing 3D models using typically unoccluded ceiling point clouds, because the ceiling is difficult to be obstructed by indoor objects and is relatively well preserved. This framework starts with extracting the roof, roof is extracted by a proposed method based on cloth simulation which can extract various shape of roofs. Then a room instance map of the scene is obtained by input the point cloud density map to Mask R-CNN. By overlapping the roof point cloud and room instance map, the whole roof is segmented into roof points of different rooms. Next, we extracted the contours of every room's roof and a new algorithm was proposed to detect connections between different rooms to restore the topological relationship between rooms, generating the scene's floor plan. Finally, by detecting the contours and connections' height, the floor plan is stretched to detected height to generate a 3D model. To evaluate the proposed method's effectiveness, experiments were conducted on two indoor LiDAR point cloud datasets, GibLayout and ISPRS Benchmark, comprising a total of 13 scenes. The results were compared with representative methods, including Floor-sp and Heat for floor plan extraction, and Polyfit and KSR for 3D model reconstruction. The experimental results show that the new method outperform existing approaches, enabling the reconstruction of highly overlapping 3D models of real scenes.

Functionalization of silk with actinomycins from Streptomyces anulatus BV365 for biomedical applications.

Silk, traditionally acclaimed as the "queen of fiber," has been widely used thanks to its brilliant performance such as gentleness, smoothness and comfortableness. Owing to its mechanical characteristics and biocompatibility silk has a definitive role in biomedical applications, both as fibroin and fabric. In this work, the simultaneous dyeing and functionalization of silk fabric with pigments from Streptomyces anulatus BV365 were investigated. This strain produced high amounts of orange extracellular pigments on mannitol-soy flour agar, identified as actinomycin D, C2 and C3. The application of purified actinomycins in the dyeing of multifiber fabric was assessed. Actinomycins exhibited a high affinity towards protein fibers (silk and wool), but washing durability was maintained only with silk. Acidic condition (pH5) and high temperature (65°C) facilitated the silk dyeing. The morphologies and chemical components of the treated silk fabrics were analyzed using scanning electron microscopy and Fourier transform infrared spectroscopy. The results showed the pigments bind to the silk through interaction with the carbonyl group in silk fibroin rendering the functionalized, yet surface that does not cause skin irritation. The treated silk exhibited a remarkable antibacterial effect, while the biocompatibility test performed with 3D-reconstructed human epidermis model indicated safe biological properties, paving the way for future application of this material in medicine.

Research on Point Cloud Acquisition and Calibration of Deep Hole Inner Surfaces Based on Collimated Ring Laser Beams.

In this study, a ring light point cloud calibration technique based on collimated laser beams is developed, aiming to reduce errors caused by the position and attitude changes of traditional ring light measurement devices. This article details the generation mechanism of the ring beam and the principle of deep hole measurement. It introduces the collimated beam as a reference, building on traditional ring light measurement devices, to achieve the synchronous acquisition of the ring beam and collimated spot images by an industrial camera. The Steger algorithm is employed to accurately extract the coordinates of the point cloud contours of both the ring beam and the collimated spot. By analyzing the shape and position changes of the collimated spot contour, the spatial position and attitude of the measuring device are precisely determined. This technique is applied to the 3D reconstruction of the inner surface of deep holes, ensuring the accurate restoration of the spatial positional attitude of the ring beam by incorporating the spatial positional attitude parameters of the measuring device to precisely calibrate the cross-sectional point cloud coordinates. Experimental results with ring gauges and deep hole workpieces demonstrate that this technique effectively reduces the percentage of point cloud data outside the tolerance range, and improves the accuracy of the 3D reconstruction model by 6.287%, thereby verifying the accuracy and practicality of this technique.

Research on New Method for Safety Testing of Steel Structures—Combining 3D Laser Scanning Technology with FEA

This paper introduces a novel approach to assessing structural safety, specifically aimed at evaluating the safety of existing structures. Firstly, a point cloud model of the existing commercial complex was captured utilizing three-dimensional (3D) laser scanning technology. Subsequently, an intelligent method for identifying holes within the point cloud model was proposed, built upon a YOLO v5-based framework, to ascertain the dimensions and locations of holes within the commercial complex. Secondly, Poisson surface reconstruction, coupled with partially self-developed algorithms, was employed to reconstruct the surface of the structure, facilitating the three-dimensional geometric reconstruction of the commercial complex. Lastly, a finite element model of the framed structure with holes was established using the reconstructed 3D model, and a safety analysis was conducted. The research findings reveal that the YOLO v5-based intelligent hole identification method significantly enhances the level of intelligence in point cloud data processing, reducing manual intervention time and boosting operational efficiency. Furthermore, through Poisson surface reconstruction and the self-developed algorithms, we have successfully achieved automated surface reconstruction, where the resulting geometric model accurately reflects the dimensional information of the commercial complex. Additionally, the maximum uniformly distributed surface load that the floor slabs within the framed structure with holes can withstand should not exceed 17.7 kN/m2, and its vertical deformation resistance stiffness is approximately 71.6% of that of a frame without holes.

OpenECAD: An efficient visual language model for editable 3D-CAD design

Computer-aided design (CAD) tools are utilized in the manufacturing industry for modeling everything from cups to spacecraft. These programs are complex to use and typically require years of training and experience to master. Structured and well-constrained 2D sketches and 3D constructions are crucial components of CAD modeling. A well-executed CAD model can be seamlessly integrated into the manufacturing process, thereby enhancing production efficiency. Deep generative models of 3D shapes and 3D object reconstruction models have garnered significant research interest. However, most of these models produce discrete forms of 3D objects that are not editable. Moreover, the few models based on CAD operations often have substantial input restrictions. In this work, we fine-tuned pre-trained models to create OpenECAD models (0.55B, 0.89B, 2.4B and 3.1B), leveraging the visual, logical, coding, and general capabilities of visual language models. OpenECAD models can process images of 3D designs as input and generate highly structured 2D sketches and 3D construction commands, ensuring that the designs are editable. These outputs can be directly used with existing CAD tools’ APIs to generate project files. To train our network, we created a series of OpenECAD datasets. These datasets are derived from existing public CAD datasets, adjusted and augmented to meet the specific requirements of vision language model (VLM) training. Additionally, we have introduced an approach that utilizes dependency relationships to define and generate sketches, further enriching the content and functionality of the datasets.

Morphological changes in flatfoot: a 3D analysis using weight-bearing CT scans

BackgroundFlatfoot is a condition resulting from complex three-dimensional (3D) morphological changes. Most Previous studies have been constrained by using two-dimensional radiographs and non-weight-bearing conditions. The deformity in flatfoot is associated with the 3D morphology of the bone. These morphological changes affect the force line conduction of the hindfoot/midfoot/forefoot, leading to further morphological alterations. Given that a two-dimensional plane axis overlooks the 3D structural information, it is essential to measure the 3D model of the entire foot in conjunction with the definition under the standing position. This study aims to analyze the morphological changes in flatfoot using 3D measurements from weight-bearing CT (WBCT).MethodIn this retrospective comparative our CT database was searched between 4–2021 and 3–2022. Following inclusion criteria were used: Patients were required to exhibit clinical symptoms suggestive of flatfoot, including painful swelling of the medial plantar area or abnormal gait, corroborated by clinical examination and confirmatory radiological findings on CT or MRI. Healthy participants were required to be free of any foot diseases or conditions affecting lower limb movement. After applying the exclusion criteria (Flatfoot with other foot diseases), CT scans (mean age = 20.9375, SD = 16.1) confirmed eligible for further analysis. The distance, angle in sagittal/transverse/coronal planes, and volume of the two groups were compared on reconstructed 3D models using the t-test. Logistic regression was used to identify flatfoot risk factors, which were then analyzed using receiver operating characteristic curves and nomogram.ResultThe flatfoot group exhibited significantly lower values for calcaneofibular distance (p = 0.001), sagittal and transverse calcaneal inclination angle (p < 0.001), medial column height (p < 0.001), sagittal talonavicular coverage angle (p < 0.001), and sagittal (p < 0.001) and transverse (p = 0.015) Hibb angle. In contrast, the sagittal lateral talocalcaneal angle (p = 0.013), sagittal (p < 0.001) and transverse (p = 0.004) talocalcaneal angle, transverse talonavicular coverage angle (p < 0.001), coronal Hibb angle (p < 0.001), and sagittal (p < 0.001) and transverse (p = 0.001) Meary’s angle were significantly higher in the flatfoot group. The sagittal Hibb angle (B = − 0.379, OR = 0.684) and medial column height (B = − 0.990, OR = 0.372) were identified as significant risk factors for acquiring a flatfoot.ConclusionThe findings validate the 3D spatial position alterations in flatfoot. These include the abduction of the forefoot and prolapse of the first metatarsal proximal, the arch collapsed, subluxation of the talonavicular joint in the midfoot, adduction and valgus of the calcaneus, adduction and plantar ward movement of the talus in the hindfoot, along with the first metatarsal’s abduction and dorsiflexion in the forefoot.

The Review of Research on 3D Model Reconstruction Based on Point Cloud Data

The Review of Research on 3D Model Reconstruction Based on Point Cloud Data

Molecular Structural Characteristics and 3D Model Reconstruction of Organic Matter in Longmaxi Formation Shale.

The establishment of molecular structure modeling is an important means to study the pore characteristics of shale organic matter and is significant for molecular-level simulations of gas storage and diffusion. Using 13C NMR, FTIR, and XPS combined with the split-peak fitting technique, the structural characteristics of the aromatic structure, aliphatic structure, and oxygen functional groups of kerogen from the shale of the Longmaxi Formation, Wuxi County, Chongqing Municipality, were quantitatively characterized. A macromolecular structure model of the kerogen was also constructed by using the 2D macromolecular structure model construction method in combination with elemental analysis experiments. The results showed that the 2D single-molecule structural model of the sample consisted of 2 benzenes, 2 naphthalenes, 1 anthracene, 5 pyrenes, 1 pyridine, and 1 pyrrole. The C skeleton types were 93 protonated arylons, 39 bridged arylons, 6 carboxylons, 5 alkyl-substituted carbons, 2 oxygen-substituted carbons, 4 methylene carbons, and 3 methylons. The established 2D molecular structure formula was C152H82O12N2. The final 3D macromolecular structure model consisted of 14 2D molecular structures (structural formula C2128H1148O168N28), with the density set to 1.77 cm3/g, compressed in a cubic cell with an edge length of 3.05 nm. Finally, the adsorption results showed that the experimental adsorption of CO2 adsorption was less than the simulated adsorption, completing the validation of the model. The above study provides a method for determining the molecular structure of kerogen in the Longmaxi Formation shale, which can guide the study of the pore structure characteristics of the Longmaxi Formation shale.

3D Data Processing and Entropy Reduction for Reconstruction from Low-Resolution Spatial Coordinate Clouds in a Technical Vision System.

This paper proposes an advancement in the application of a Technical Vision System (TVS), which integrates a laser scanning mechanism with a single light sensor to measure 3D spatial coordinates. In this application, the system is used to scan and digitalize objects using a rotating table to explore the potential of the system for 3D scanning at reduced resolutions. The experiments undertaken searched for optimal scanning windows and used statistical data filtering techniques and regression models to find a method to generate a 3D scan that was still recognizable with the least amount of 3D points, balancing the number of points scanned and time, while at the same time reducing effects caused by the particularities of the TVS, such as noise and entropy in the form of natural distortion in the resulting scans. The evaluation of the experimentation results uses 3D point registration methods, joining multiple faces from the original volume scanned by the TVS and aligning it to the ground truth model point clouds, which are based on a commercial 3D camera to verify that the reconstructed 3D model retains substantial detail from the original object. This research finds it is possible to reconstruct sufficiently detailed 3D models obtained from the TVS, which contain coarsely scanned data or scans that initially lack high definition or are too noisy.

Implantation of S1AIS has priority as a sacroiliac joint fixation technique.

The sacral alar-iliac screw (SAIS) fixation technique has evolved from spinopelvic fixation which originated from S2AIS to sacroiliac joint fixation, with more reports regarding its application of S2AIS than S1AIS. However, there is a lack of comparative evidence to determine which technique is superior for sacroiliac joint fixation. This study aimed to determine which of the screws was superior in terms of implantation safety and biomechanical stability for sacroiliac joint fixation. CT data of 80 normal pelvises were analyzed to measure the insertable range, trajectory lengths and widths of both S1AIS and S2AIS on 3D reconstruction models. Φ 6.5mm and 8.0mm screws were implanted on the left and right sides of fifty 3D printed pelvic models respectively to observe for breach of screw implantation. Ten synthetic pelvis models were used to simulate type C Tile injuries, and divided into 2 groups with an anterior plate and posterior fixation using one S1AIS or S2AIS on each side. The stiffness and maximum load of the plated and fixated models were measured under vertical loading. The trajectory lengths and widths of the S1AIS and S2AIS were similar (p > 0.05) and there was no breach for Φ 6.5mm SAIS. However, both the insertable range and trajectory length on the sacral side of S2AIS (234.56 ± 10.06mm2, 40.97 ± 2.81mm) were significantly less, and the breach rate of the posterior lateral cortex of the Φ 8.0mm S2AIS (46%) was significantly higher than the S1AIS (307.55 ± 10.42 mm2, 42.16 ± 3.06mm, and 2%, p < 0.05). The stiffness and maximum load of S2AIS were less than S1AIS but the difference was not statistically significant (p > 0.05). S1AIS and S2AIS have similar screw trajectories and stability. However, S1AIS has a larger insertable range, less breach of the posterior lateral sacral cortex and longer trajectory length on the sacral side than S2AIS, which indicates S1AIS has higher implantation safety and a trend of better mechanical performance over S2AIS for sacroiliac joint fixation. Furthermore, S2AIS with an excessively large diameter should be used with caution for sacroiliac joint fixation.

Cross-section extraction and model reconstruction of lava tube based on L1-medial skeleton

Lunar lava tube caves, due to their unique geographical structure and potential as shelters, have repeatedly emerged as prime candidates or priority areas during discussions on lunar base site selection. However, owing to current technological limitations and the complexities of lunar exploration, obtaining detailed data directly from the interiors of lunar lava tubes poses a significant challenge. Hence, the study of terrestrial lava tubes assumes critical importance. By delving into Earth’s lava tubes, we can gain insights into crucial aspects such as their formation mechanisms, geological characteristics, and stability, which can then inform and guide research on lava tubes found on extraterrestrial bodies like the Moon and Mars. During the data acquisition process for lava tubes, given their intricate spatial configurations and dim lighting conditions, we have opted to employ LiDAR technology to collect point cloud data, revealing the internal structural information of the lava tubes. Consequently, this paper thoroughly explores a method for extracting lava tube parameters and constructing models using this point cloud data. This approach encompasses four key steps: data preprocessing, axis construction of the lava tube, cross-sectional feature extraction, and 3D model reconstruction. Initially, raw data is refined through the creation of point cloud voxels to mitigate noise interference. Subsequently, an iterative algorithm is utilized to obtain the L1-medial skeleton, enabling precise capture of the lava tube’s axis information. Using this extracted axis, we proceed with sampling at fixed intervals to derive cross-sectional points of the lava tube. These points are then utilized to extract fundamental geometric parameters such as cross-sectional area, length, and width. Ultimately, by integrating all these cross-sectional point cloud data, a comprehensive 3D model of the lava tube is constructed. Remarkably, this method achieves a high degree of accuracy, with 99 % of the extracted cross-sectional points exhibiting an error of less than 0.12 m, and 99 % of the model’s positional errors falling within 0.15 m. This not only enhances the precision of parameter extraction but also provides significant technical support for in-depth studies of extraterrestrial volcanic activities and lava tube formation mechanisms. Furthermore, it offers a valuable tool for parameter extraction and research on other irregular natural tunnels.