3D Reconstruction Research Articles

Protocol for dissection of Drosophila abdomens for fluorescent imaging

Fluorescent imaging in Drosophila species is indispensable for investigating dynamic biological processes and visualizing gene expression patterns with cellular precision. This technique leverages the transparency of Drosophila larvae and pupae, combined with advanced microscopy, to enable real-time observation of developmental events such as morphogenesis and organogenesis. Genetically encoded fluorescent proteins and dyes allow specific labeling of cells and proteins, facilitating detailed studies of spatial and temporal dynamics within intact tissues. Techniques like confocal and two-photon microscopy provide high resolution and depth penetration, essential for 3D reconstruction and quantitative analysis of complex biological structures. Fluorescent imaging in Drosophila supports disease modeling, drug screening, and therapeutic exploration, bridging insights from basic biology to potential clinical applications. It is therefore necessary to develop imaging techniques and protocols that accurately capture and profile gene expression patterns in a wide range of Drosophila tissues. In this study, we present a detailed protocol for preparing and imaging transgenic Drosophila abdomen, which will enable researchers investigate gene expression patterns underlying fundamental biological processes in the abdomen.

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.

Green landscape 3D reconstruction and VR interactive art design experience using digital entertainment technology and entertainment gesture robots

With the rapid development of digital entertainment technology, people’s demand for green landscape reconstruction and virtual reality (VR) interactive art design is increasing. This study aims to explore the use of digital entertainment technology and entertainment gesture robots, combined with green landscape 3D reconstruction and VR interactive art design, to provide a new art experience. This paper uses digital entertainment technology to reconstruct the green landscape in 3D, and combines the interactive function of entertainment gesture robot to enable users to interact with the virtual environment. Then VR technology is used to present the reconstructed landscape to the user, and through immersive experience, users can feel the immersive art design. Through experimental evaluation and user survey, 3D reconstruction of green landscape and VR interactive art design using digital entertainment technology and entertainment gesture robots can provide a new entertainment experience. Users are more satisfied with the immersive art design expression, and experience the interaction and connection with the natural environment, which provides users with a unique and immersive art experience, further enriching the form and content of digital entertainment.

A Super-Resolution and 3D Reconstruction Method Based on OmDF Endoscopic Images.

In the field of endoscopic imaging, challenges such as low resolution, complex textures, and blurred edges often degrade the quality of 3D reconstructed models. To address these issues, this study introduces an innovative endoscopic image super-resolution and 3D reconstruction technique named Omni-Directional Focus and Scale Resolution (OmDF-SR). This method integrates an Omnidirectional Self-Attention (OSA) mechanism, an Omnidirectional Scale Aggregation Group (OSAG), a Dual-stream Adaptive Focus Mechanism (DAFM), and a Dynamic Edge Adjustment Framework (DEAF) to enhance the accuracy and efficiency of super-resolution processing. Additionally, it employs Structure from Motion (SfM) and Multi-View Stereo (MVS) technologies to achieve high-precision medical 3D models. Experimental results indicate significant improvements in image processing with a PSNR of 38.2902 dB and an SSIM of 0.9746 at a magnification factor of ×2, and a PSNR of 32.1723 dB and an SSIM of 0.9489 at ×4. Furthermore, the method excels in reconstructing detailed 3D models, enhancing point cloud density, mesh quality, and texture mapping richness, thus providing substantial support for clinical diagnosis and surgical planning.

3DGEN: a framework for generating custom-made synthetic 3D datasets for civil structure health monitoring

The availability of high-quality datasets is increasingly critical in the field of computer vision-based civil structural health monitoring, where deep learning approaches have gained prominence. However, the lack of specialized datasets for such tasks poses a significant challenge for training a reliable model. To address this challenge, a framework, 3DGEN, is proposed to swiftly generate realistic synthetic 3D datasets which can be targeted for specific tasks. The framework is based on diverse 3D civil structural models, rendering them from various angles and providing depth information and camera parameters for training neural networks. By employing mathematical methods, such as analytical solutions and/or numerical simulations, deformation of civil engineering structures can be generated, ensuring a reliable representation of their real-world shapes and characteristics in the 3D datasets. For texture generation, a generative 3D texturing method enables users to specify desired textures using plain English sentences. Two successful experiments are conducted to (1) assess the efficiency of generating the 3D datasets using two distinct structures, (2) train a monocular depth estimation network to perform 3D surface reconstruction with the generated dataset. Notably, 3DGEN is not limited to 3D surface reconstruction; it can also be used for training neural networks for various other tasks. The code and dataset are available at: https://github.com/YANDA-SHAO/Beam-Dataset-SE

Voxel-based 3D reconstruction of additively manufactured open porous structures for CFD simulation

With the ability to manufacture complex functional structures, Additive Manufacturing (AM) enables new advanced applications which could not be realized before. An example of such structures is open porous structures. In the presented approach, the open porous structures are a result of the selected manufacturing parameters, instead of being defined in CAD models. Thereby, various porosities can be achieved flexibly without design adjustments. However, to comprehensively understand the correlation of manufacturing parameters and different properties, various tests are required. As an alternative, properties can be determined by means of simulation. This study presents 3D reconstructions of complex open porous structures, which are based on μ-CT imaging. The influence of the downsampling during voxelization on the 3D reconstructed geometry is studied. Further, different polyhedral mesh settings have been tested in order to find a mesh that minimizes the deviation between the CFD simulation results and the laboratory test results for flow resistance. A guideline for the creation of a calibrated CFD model based on μ-CT imaging is proposed. As it is acknowledged by the authors that the data availability to reconstruct shown cases is crucial, data for a reference case have been made freely available online.Graphical

TVNeRF: Improving few-view neural volume rendering with total variation maximization

Neural Radiance Fields (NeRF) has profoundly impacted few-shot novel view synthesis and 3D reconstruction with its innovative rendering techniques and high-quality output. However, synthesizing novel views from sparse inputs remains challenging for NeRF. With limited view inputs, NeRF tends to overfit the available views, leading to reconstruction discrepancies and artifacts. To address this issue, we propose TVNeRF, which introduces two complementary regularization methods. By maximizing the total variation of unseen rays, we control the density distribution of ray sampled points to prevent excessive sampling in non-critical areas, thereby reducing artifacts and floaters. Concurrently, minimizing the accumulated opacity of unseen rays allows for the effective selection of important rays and sampled points without additional computational overhead, while also mitigating potential over-regularization effects. These combined strategies not only prevent overfitting but also achieve effects similar to the Least Absolute Shrinkage and Selection Operator (LASSO) regularization. Moreover, TVNeRF achieves these improvements without the need for pre-trained models or prior knowledge. By utilizing these straightforward yet effective regularization techniques, TVNeRF can deliver results comparable to state-of-the-art models while maintaining computational efficiency. This approach highlights that substantial improvements in NeRF performance can be realized through targeted regularization methods, effectively addressing the challenges posed by sparse view synthesis.

Computer Vision Algorithm Design in Image Processing Based on Projective Geometry

Image processing with computer vision, particularly in the realm of projective geometry, offers remarkable potential for various applications. Through the lens of projective geometry, images can be transformed, augmented, and reconstructed with precision, facilitating tasks such as image rectification, 3D reconstruction, and object tracking. Landmark estimation in computer vision is a vital task with broad applications across various domains. This process involves identifying key points or landmarks within images, enabling tasks such as facial recognition, object tracking, and gesture recognition. This paper, proposed a novel approach for landmark estimation in computer vision using Projective Geometry Landmark Estimation (PGLM). The proposed model aims to estimate the landmark features by a projective geometry model. With the estimation of the geometry features landmarks related to the facial, object, and medical images are computed. The PGLM model uses the point features for the location of the landmark features. In order to compare PGLM's performance to that of more conventional classification methods like Random Forest, K-Nearest Neighbors (KNN), and Support Vector Machine (SVM), simulation analysis is carried out. From what we can see, PGLM routinely beats these alternatives when we compare their accuracy, precision, recall, and F1 score. The findings stated the effectiveness of PGLM as a promising approach for landmark estimation in image processing tasks, paving the way for further advancements in this domain.

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.

Tunneling nanotube-driven complete regeneration of murine fetal skin

This study investigated the three-dimensional (3D) cellular interactions and tunneling nanotubes (TNTs) during fetal mouse skin regeneration on embryonic days 13 (E13) and 15 (E15). We aimed to understand spatial relationships among cell types involved in skin regeneration and assess the potential role of TNTs. Full-thickness skin incisions were performed in E13 and E15 embryos. Wound sites were collected, embedded in epoxy resin, processed for 3D reconstruction (1 μm thickness sections), and subjected to whole-mount immunostaining. We conducted in vitro co-culture experiments with fetal macrophages and fibroblasts to observe TNT formation. To assess the effect of TNTs on skin regeneration, an inhibiting agent (cytochalasin B) was administered to amniotic fluid. Results revealed that E13 epidermal keratinocytes interacted with dermal fibroblasts and macrophages, facilitating skin regrowth. TNT structures were observed at the E13-cell wound sites, among macrophages, and between macrophages and fibroblasts, confirmed through in vitro co-culture experiments. In vitro and utero cytochalasin B administration hindered those formation and inefficient skin texture regeneration at E13 wound sites. This emphasizes the necessity of 3D cellular interactions between epidermal and dermal cells during skin regeneration in mouse embryos at E13. The prevalence of TNT structures indicated their involvement in achieving complete skin texture restoration.

Water Conservancy Hub Geometric Modeling Based on Point Clouds

For hydraulic engineering, accurate geometric modeling is crucial for design, analysis, and maintenance. In recent years, point cloud technology, with its high precision and detailed data capture capabilities, has gradually become the preferred method for geometric modeling in hydraulic engineering. However, traditional point cloud processing methods exhibit slow data processing speeds and poor mesh quality when reconstructing 3D models. Therefore, we have studied the meshing method for the 3D reconstruction of large-scale discrete point cloud data and proposed a meshing method based on the Delaunay criterion and the growth method to achieve rapid meshing of discrete point cloud data. To demonstrate the efficiency and convenience of the proposed method, point cloud reconstruction experiments are conducted on various buildings. We compare our approach with the Poisson, BPA, and ONet methods. Additionally, we apply it to the Jiangsu Gaogang Water Conservancy Hub in China. The results show that our proposed method is superior in both reconstruction quality and efficiency, providing a promising solution for the reconstruction of 3D building models from point clouds.

Three-dimensional surface temperature measurement using lifetime-based phosphor thermometry

Abstract In this study, we propose a three-dimensional (3D) surface temperature measurement method based on the principle of stereoscopic 3D reconstruction and the dependence of phosphorescence lifetime on temperature. A 385-nm UV(Ultraviolet) light was used as the excitation light, and two high-speed cameras were used as the detectors. The phosphor MFG (Mg4FGeO6: Mn4+) was mixed with the binder HPC and sprayed onto the tested 3D surface. The natural texture generated by the surface roughness of the phosphor coating was used as a feature for cross-correlation calculations. The digital image correlation (DIC) algorithm was used to match these feature positions in the phosphorescent images from the two cameras. The effects of the excitation angle and detecting angle were analyzed. The results indicate that the temperature measurement based on phosphorescent lifetime was not affected by the excitation and detecting angle. The method was validated on a turbine blade as an example of a 3D surface to demonstrate the capability. A comparison of the measurement results with the thermocouples proves that the current method can successfully measure the temperature on 3D surfaces with a maximum difference of 1.63°C. The spatial accuracy of the method was obtained by comparing with the measurement results of a 3D scanner, which shows that the maximum absolute error of the 3D reconstruction was 0.350 mm. The current study proposes a promising 3D surface temperature measurement method, which is expected to be widely used in gas turbine blades, Internal Combustion (IC) engine cylinders, complex curved heat exchangers, and other fields due to its non-contact measurement, low susceptibility to infrared radiation interference, high measurement accuracy, and ability to withstand harsh environments.&amp;#xD;

Comparative microscopic anatomy of Schizomida – 2. The rostrosoma and the pharyngeal suction pump

This paper tests hypotheses of independent parallel evolution of the rostrosoma among euchelicerate taxa by analyzing the microscopic anatomy and histology of the rostrosoma of Uropygi (Schizomida and Thelyphonida) and comparing it with the morphology of the snout region in other euchelicerates. The study employs analysis of multiple histological serial sections, μCT-imaging, and graphical as well as computer-based 3D reconstruction. Results of the study are that Thelyphonida and Schizomida share the same morphology of the rostrosoma. The rostrosoma of both groups contains a unique arrangement of musculature that is functionally interpreted as pre-oral suction pump. This is followed by a pharyngeal suction pump. The muscles of the pharyngeal suction pump attach to the epistome and the epipharyngeal sclerite. Neither Schizomida nor Thelyphonida possess a postcerebral suction pump as reported earlier. The microscopic anatomy of the rostrosoma of both taxa is unique and does not compare with any of the other euchelicerates, thus supporting the idea of independent evolutionary origin of the rostrosoma. Thelyphonida, Amblypygi and Scorpiones share the occurrence of a large epipharyngeal/epistomal sclerite with associated musculature, which is a feature that lines up with the Arachnopulmonata concept. A comparison with all Euchelicerata taxa shows that the snout region is formed by homologous morphological elements but the specific arrangement, additions and reductions shape the formation of the rostrosoma, so that parallel evolution of homologous parts of the arachnid ground pattern can be assumed that has formed those elements into convergent morphologies.

Multi-factor quality assessment of digital speckle pattern for speckle projection profilometry

Abstract The digital speckle pattern (DSP) is an essential component in the speckle projection profilometry (SPP) task, its quality directly affects the results of three-dimensional (3D) shape reconstruction. However, the SPP field lacks specialized numerical metrics for evaluating speckle quality. To address this issue, this study introduces a multi-factor metric (MFM) for comprehensive DSP assessment. Through comparing the metric, optimal parameter ranges for DSP design and the advisable matching subset size can be determined for SPP algorithm. A global indicator named valid feature distribution (VFD) based on scale-invariant feature transform (SIFT) and Delaunay triangulation, is defined to analyze the overall information distribution in DSPs. In addition, MFM incorporates a local metric called mean subset intensity gradient (MSIG), which aids in selecting the suitable radius for different DSPs to balance the accuracy and efficiency. The quality assessment targets the speckle scene images, allowing for the reverse adjustment of the most suitable DSP according to different scenes. The performance of DSPs can be evaluated based on the accuracy and completeness of 3D reconstruction results. By conducting simulation experiments on the 3ds Max platform, the recommended parameter range for DSP can be inferred, including speckle density ratio, speckle diameter, and random variation rate. Appropriate subset sizes for different scenes are also investigated. Furthermore, the MFM is verified on a real binocular speckle device, demonstrating that the measurement standard deviation of a complex workpiece can be reduced to 0.078 mm using the recommended DSP.&amp;#xD;

Advancing Precision Rhinoplasty: Preoperative Digital 3D Surgical Planning.

The aim of the study is to assess the efficacy and advantages of utilizing state-of-the-art three-dimensional (3D) reconstruction technology in preoperative planning for rhinoplasty surgery. It is a study of a single rhinoplasty case that was operated at a tertiary hospital. The patient was assessed through a detailed history, blood investigations, radiological investigations and preoperative 3D (three-dimensional) reconstruction of the face and nose. The study utilized high-resolution CT scans and 3D reconstruction software like 3D (three-dimensional) Slicer and Blender 3D (three-dimensional) to analyze nasal anatomy for preoperative planning in rhinoplasty. External and internal nasal measurements were taken, and axial cross-section analysis was conducted to assess nasal structure deviation. The benefits of 3D (three-dimensional) visualization in surgical planning were evaluated, and surgical management was based on preoperative reconstructions. Comprehensive preoperative evaluations were performed, adhering to ethical guidelines.Preoperative 3D (three-dimensional) reconstruction planning methods facilitated precise surgical planning and execution in rhinoplasty with satisfactory outcomes for both the patient and the surgeon. Incorporating 3D (three-dimensional) reconstruction technology in rhinoplasty preoperative planning enhances surgical precision and patient satisfaction, ensuring optimized surgical outcomes while adhering to ethical standards.

A generative machine learning model for the 3D reconstruction of material microstructure and performance evaluation

The 3D reconstruction is generally defined as the process of capturing the shape and appearance of real objects. By reconstructing 3D digital model from a series of 2D slices, it brings considerable convenience to visualize internal structure and decipher structure-property relation of a material. Nowadays, the 3D reconstruction is becoming a cutting-edge technique in depicting the internal structure and evaluating the physical performance of targeted materials. Recent years, generative machine learning methods, such as generative adversarial networks (GAN), have achieved tremendous success in AI-generated physical content (AIGPC). However, lots of technical challenges remain, including oversimplified models, oversized dataset requirement and inefficient convergence. These difficulties are caused by the insufficient ability to capture detailed features and the inadequacy of the generated model quality. To this end, a novel generative model is developed, which combines the multiscale features of U-net and the synthesis ability of GANs. With the help of the multiscale channel aggregation module, the hierarchical feature aggregation module and the convolutional block attention module, our model is developed to capture the features of the material microstructure better. The loss function is refined by combining the image regularization loss with the Wasserstein distance loss. In addition, the anisotropy index is adopted to measure anisotropic degree of selected samples quantitively. The results demonstrate that the 3D structures generated by the proposed model retain high fidelity with ground truth samples. With remarkable performance, the proposed model not only overtakes traditional GAN, but will shed a brilliant light on AIGPC and physical 3D reconstruction.

A Segment-Based Algorithm for Grid Junction Corner Detection Used in Stereo Microscope Camera Calibration

Corner detection is responsible for accurate camera calibration, which is an essential task for binocular three-dimensional (3D) reconstruction. In microscopic scenes, binocular 3D reconstruction has significant potential to achieve fast and accurate measurements. However, traditional corner detectors and calibration patterns (checkerboard) performed poorly in microscopic scenes due to the non-uniform illumination and the shallow depth of field of the microscope. In this paper, we present a novel method for detecting grid junction corners based on image segmentation, offering a robust alternative to the traditional checkerboard pattern. Model fitting was utilized to obtain the coordinates at a sub-pixel level. The procedures of the proposed method were elaborated, including image segmentation, corner prediction, and model fitting. The mathematical model was established to describe the grid junction. The experiment was conducted using both synthetic and real data and the experimental result shows that this method achieves high precision and is robust to image blurring, indicating this method is suitable for microscope camera calibration.

Detection and Utilization of Reflections in LiDAR Scans through Plane Optimization and Plane SLAM.

In LiDAR sensing, glass, mirrors and other materials often cause inconsistent data readings from reflections. This causes problems in robotics and 3D reconstruction, especially with respect to localization, mapping and, thus, navigation. Extending our previous work, we construct a global, optimized map of reflective planes, in order to then classify all LiDAR readings at the end. For this, we optimize the reflective plane parameters of the plane detection of multiple scans. In a further method, we apply the reflective plane estimation in a plane SLAM algorithm, highlighting the applicability of our method for robotics. As our experiments will show, this approach provides superior classification accuracy compared to the single scan approach. The code and data for this work are available as open source online.

Texture tomography, a versatile framework to study crystalline texture in 3D.

Crystallographic texture is a key organization feature of many technical and biological materials. In these materials, especially hierarchically structured ones, the preferential alignment of the nano constituents heavily influences the macroscopic behavior of the material. To study local crystallographic texture with both high spatial and angular resolution, we developed Texture Tomography (TexTOM). This approach allows the user to model the diffraction data of polycrystalline materials using the full reciprocal space of the crystal ensemble and describe the texture in each voxel via an orientation distribution function, hence it provides 3D reconstructions of the local texture by measuring the probabilities of all crystal orientations. The TexTOM approach addresses limitations associated with existing models: it correlates the intensities from several Bragg reflections, thus reducing ambiguities resulting from symmetry. Further, it yields quantitative probability distributions of local real space crystal orientations without further assumptions about the sample structure. Finally, its efficient mathematical formulation enables reconstructions faster than the time scale of the experiment. This manuscript presents the mathematical model, the inversion strategy and its current experimental implementation. We show characterizations of simulated data as well as experimental data obtained from a synthetic, inorganic model sample: the silica-witherite biomorph. TexTOM provides a versatile framework to reconstruct 3D quantitative texture information for polycrystalline samples; it opens the door for unprecedented insights into the nanostructural makeup of natural and technical materials.

3D Reconstruction of Neurons with Selective Synaptic Connectivity in the Ventral Tegmental Area by Corelative Light and Electron Microscopy

3D Reconstruction of Neurons with Selective Synaptic Connectivity in the Ventral Tegmental Area by Corelative Light and Electron Microscopy