3D Model Reconstruction Research Articles

The role of spinal cord neuroanatomy in the variances of epidural spinal recordings

BackgroundSpinal cord stimulation (SCS) has demonstrated multiple benefits in treating chronic pain and other clinical disorders related to sensorimotor dysfunctions. However, the underlying mechanisms are still not fully understood, including how electrode placement in relation to the spinal cord neuroanatomy influences epidural spinal recordings (ESRs). To characterize this relationship, this study utilized stimulation applied at various anatomical sections of the spinal column, including at levels of the intervertebral disc and regions correlating to the dorsal root entry zone.MethodTwo electrode arrays were surgically implanted into the dorsal epidural space of the swine. The stimulation leads were positioned such that the caudal-most electrode contact was at the level of a thoracic intervertebral segment. Intraoperative cone beam computed tomography (CBCT) images were utilized to precisely determine the location of the epidural leads relative to the spinal column. High-resolution microCT imaging and 3D-model reconstructions of the explanted spinal cord illustrated precise positioning and dimensions of the epidural leads in relation to the surrounding neuroanatomy, including the spinal rootlets of the dorsal and ventral columns of the spinal cord. In a separate swine cohort, implanted epidural leads were used for SCS and recording evoked ESRs.ResultsReconstructed 3D-models of the swine spinal cord with epidural lead implants demonstrated considerable distinctions in the dimensions of a single electrode contact on a standard industry epidural stimulation lead compared to dorsal rootlets at the dorsal root entry zone (DREZ). At the intervertebral segment, it was observed that a single electrode contact may cover 20-25% of the DREZ if positioned laterally. Electrode contacts were estimated to be ~0.75 mm from the margins of the DREZ when placed at the midline. Furthermore, ventral rootlets were observed to travel in proximity and parallel to dorsal rootlets at this level prior to separation into their respective sides of the spinal cord. Cathodic stimulation at the level of the intervertebral disc, compared to an ‘off-disc’ stimulation (7 mm rostral), demonstrated considerable variations in the features of recorded ESRs, such as amplitude and shape, and evoked unintended motor activation at lower stimulation thresholds. This substantial change may be due to the influence of nearby ventral roots. To further illustrate the influence of rootlet activation vs. dorsal column activation, the stimulation lead was displaced laterally at ~2.88 mm from the midline, resulting in variances in both evoked compound action potential (ECAP) components and electromyography (EMG) components in ESRs at lower stimulation thresholds.ConclusionThe results of this study suggest that the ECAP and EMG components of recorded ESRs can vary depending on small differences in the location of the stimulating electrodes within the spinal anatomy, such as at the level of the intervertebral segment. Furthermore, the effects of sub-centimeter lateral displacement of the stimulation lead from the midline, leading to significant changes in electrophysiological metrics. The results of this pilot study reveal the importance of the small displacement of the electrodes that can cause significant changes to evoked responses SCS. These results may provide further valuable insights into the underlying mechanisms and assist in optimizing future SCS-related applications.

Three-dimensional artistic design method of ceramic products based on recurrent neural network technology

Three-dimensional digital technology has made breakthroughs and shown unique advantages in all walks of life. On the basis of practicality, the three-dimensional artistic design of ceramic products gradually adds some aesthetic, artistic design elements, which brings beautiful enjoyment to people’s lives and makes people’s lives colorful. This paper presents a three-dimensional artistic design method for ceramic products based on RNN (recurrent neural network) technology. With the establishment of the 3D YOLOv3 framework, the new model training is faster and more stable, the convergence speed of the loss function is faster, and the reconstructed 3D model is more accurate. After training for a certain number of times, the network gradually becomes stable, the accuracy rate is kept at 95%, and the loss function value is reduced below 0.2. The accuracy of the network model and the precision of semantic segmentation are improved. The semantic segmentation and object recognition under 3D scene reconstruction studied in this paper have certain theoretical value and high feasibility.

A fast point cloud registration method based on spatial relations and features

Abstract Point cloud registration plays a crucial role in mobile robot localization, map building and three-dimensional (3D) model reconstruction. However, it remains challenged by issues such as compromised accuracy and sluggish efficiency, posing significant obstacles in achieving precise and timely alignments. Therefore, we propose a lightweight and fast point cloud registration method. Firstly, we mesh the 3D point cloud, compared with the traditional gridded point cloud method, it achieves initial point cloud registration by preserving the curvature characteristics of the internal point cloud, and utilizing the spatial relationship between grid cells and the quantitative relationship between the internal point cloud. Moreover, we adopt an iterative nearest point based on KD-Tree to realize the fine registration. So, our method does not necessitate intricate feature analysis and data training, and is resilient to similar transformations, non-uniform densities and noise. Finally, we conduct point cloud registration experiments using multiple publicly available point cloud datasets and compare them with several point cloud registration methods. The results demonstrate it is able to accomplish the point cloud registration quickly and exhibit high accuracy. More importantly, it maintains its efficacy and robustness even in the presence of noisy and defective point clouds.

Large dump critical zone of interest identification and slope failure prediction using realistic 3D numerical modelling

Dump failure is one of the most catastrophic hazards in the opencast mining industry and involves sudden and violent failures. Therefore, susceptible region identification is essential for slope failure mitigation in the large dump. This study proposed a sustainable approach for large dump critical zone of interest (CZI) identification, stability monitoring and slope failure prediction. UAV close-range photogrammetry survey and structure-from-motion (SfM) approach were used for the dump realistic 3D model reconstruction. 3D numerical modelling was employed for dump stability analysis. In this investigation, the dump critical zone, slope factor of safety (FOS), displacement and slope failure prediction were analysed using the 3D numerical modelling. The results indicate that maximum displacement was found in the critical zone I. However, the dump was stable under static conditions. Dump critical slope failure time is predicted using the inverse velocity method. The results of this investigation are reliable and establish the scope of UAV photogrammetry for realistic 3D modelling. In addition, the slope stability state and failure model were consistent with field observations. The proposed approach can be used for the mine, hill, and roadcut slopes. UAV technology and 3D modelling approaches have substantially reduced data collection time and expense.

An accurate measurement of high-reflective objects by using 3D structured light

Using three-dimensional structured light to measure high-reflective objects accurately is a big challenge. Some overexposed and blurred areas in the structured light fringe patterns lead to the loss of semantic and texture information on the object surface, further leading to 3D model reconstruction errors. To solve this problem, a lightweight novel network, Deformable Convolutional and Multi-scale Convolutional Network (DcMcNet), is proposed for repairing highly reflectively distorted regions in sinusoidal fringe patterns and the Gray code binary fringe patterns. DcMcNet uses deformable convolution and multi-scale convolution to realize the effective utilization of multiscale global features in repairing highly reflective distorted regions and can adapt well to the different shapes of these regions. In addition, virtual software is used to acquire large-scale and high-fidelity datasets and a diverse loss function is constructed to better optimize DcMcNet. Depth reconstruction experiments of sinusoidal fringe patterns and point cloud reconstruction experiments of Gray code binary fringe patterns show that our method can repair highly reflective distortion regions with high accuracy, and for aeronautical blades, the MAE of the depth map is reduced from 0.1608 to 0.0675, and the point cloud coverage is improved from 76.64% to 98.95%.

Digital reconstruction of three‐dimensional contours and its application to microstructural evaluation of postblast rock fissure surfaces

AbstractFracture surface contour study is one of the important requirements for characterization and evaluation of the microstructure of rocks. Based on the improved cube covering method and the 3D contour digital reconstruction model, this study proposes a quantitative microstructure characterization method combining the roughness evaluation index and the 3D fractal dimension to study the change rule of the fracture surface morphology after blasting. This method was applied and validated in the study of the fracture microstructure of the rock after blasting. The results show that the fracture morphology characteristics of the 3D contour digital reconstruction model have good correlation with the changes of the blasting action. The undulation rate of the three‐dimensional surface profile of the rock is more prone to dramatic rise and dramatic fall morphology. In terms of tilting trend, the tilting direction also shows gradual disorder, with the tilting angle increasing correspondingly. All the roughness evaluation indexes of the rock fissure surface after blasting show a linear and gradually increasing trend as the distance to the bursting center increases; the difference between the two‐dimensional roughness evaluation indexes and the three‐dimensional ones of the same micro‐area rock samples also becomes increasingly larger, among which the three‐dimensional fissure roughness coefficient JRC and the surface roughness ratio Rs display better correlation. Compared with the linear fitting formula of the power function relationship, the three‐dimensional fractal dimension of the postblast fissure surface is fitted with the values of JRC and Rs, which renders higher correlation coefficients, and the degree of linear fitting of JRC to the three‐dimensional fractal dimension is higher. The fractal characteristics of the blast‐affected region form a unity with the three‐dimensional roughness evaluation of the fissure surface.

A comparative study on 3D printing-assisted arthroscopic IDEAL point femoral tunnel positioning for anterior cruciate ligament reconstruction versus conventional arthroscopic positioning

BackgroundThis study aimed to investigate the feasibility and precision of using a 3D-printed template for femoral tunnel placement in guiding the optimal positioning of the Internal anatomical stop and Low tension maintenance (IDEAL) bone tunnel during single-bundle anterior cruciate ligament (ACL) reconstruction.MethodsA retrospective analysis was conducted on 40 patients who underwent arthroscopic single-bundle ACL reconstruction at our hospital between April 2021 and November 2021. In the direct vision group, the IDEAL bone tunnel was positioned using radiofrequency localization directly visualized at the stump. In the 3D-printed positioning group, preoperative CT scans and Digital Imaging and Communications in Medicine (DICOM) data were employed. Following the Quadrant method by Bernard, the femoral tunnel’s depth was set at 25% and its height at 29%. Postoperative plain CT scans enabled the reconstruction of 3D models for both groups. The accuracy of femoral tunnel placement was then compared.ResultsThe central locations of the bone tunnels in the direct vision group were at a mean depth of 25.74 ± 1.84% and a height of 29.22 ± 2.97%. In the 3D printing localization group, these values were 25.39 ± 2.98% for depth and 28.89 ± 2.50% for height, respectively. No significant differences were found in tunnel positioning between the groups. Both groups demonstrated statistically significant improvements in International Knee Documentation Committee Subjective Knee Form (IKDC) and Lysholm scores postoperatively, with no significant differences observed 12 months post-surgery.ConclusionThe findings of this study suggest that 3D printing-assisted arthroscopic IDEAL point femoral tunnel positioning and conventional arthroscopic positioning are feasible and effective for ACL reconstruction. Using 3D printing technology to design femoral anchor points in ACL reconstruction allows for the customization of anterior fork reconstruction and precise bone tunnel positioning, supporting the goal of individualized and accurate reconstruction.

Three-Dimensional Bowing Measurement of Distal Femur at Actual Size and Clinical Implications of Total Knee Arthroplasty.

Background and Objectives: To assess femoral shaft bowing (FSB) in coronal and sagittal planes and introduce the clinical implications of total knee arthroplasty (TKA) by analyzing a three-dimensional (3D) model with virtual implantation of the femoral component. Materials and Methods: Sixty-eight patients (average age: 69.1 years) underwent 3D model reconstruction of medullary canals using computed tomography (CT) data imported into Mimics® software (version 21.0). A mechanical axis (MA) line was drawn from the midportion of the femoral head to the center of the intercondylar notch. Proximal/distal straight centerlines (length, 60 mm; diameter, 1 mm) were placed in the medullary canal's center. Acute angles between these centerlines were measured to assess lateral and anterior bowing. The acute angle between the distal centerline and MA line was measured for distal coronal and sagittal alignment in both anteroposterior (AP) and lateral views. The diameter of curve (DOC) along the posterior border of the medulla was measured. Results: The mean lateral bowing in the AP view was 3.71°, and the mean anterior bowing in the lateral view was 11.82°. The average DOC of the medullary canal was 1501.68 mm. The average distal coronal alignment of all femurs was 6.40°, while the distal sagittal alignment was 2.66°. Overall, 22 femurs had coronal bowing, 42 had sagittal bowing, and 15 had both. Conclusions: In Asian populations, FSB can occur in coronal, sagittal, or both planes. Increased anterolateral FSB may lead to cortical abutment in the sagittal plane, despite limited space in the coronal plane. During TKA, distal coronal alignment guides the distal femoral valgus cut angle, whereas distal sagittal alignment aids in predicting femoral component positioning to avoid anterior notching. However, osteotomies along the anterior cortical bone intended to prevent notching may result in outliers due to differences between the distal sagittal alignment and the distal anterior cortical axis.

High Fidelity Makeup via 2D and 3D Identity Preservation Net

In this article, we address the challenging makeup transfer task, aiming to transfer makeup from a reference image to a source image while preserving facial geometry and background consistency. Existing deep neural network-based methods have shown promising results in aligning facial parts and transferring makeup textures. However, they often neglect the facial geometry of the source image, leading to two adverse effects: (1) alterations in geometrically relevant facial features, causing face flattening and loss of personality, and (2) difficulties in maintaining background consistency, as networks cannot clearly determine the face-background boundary. To jointly tackle these issues, we propose the High Fidelity Makeup via two-dimensional (2D) and 3D Identity Preservation Network (IP23-Net), to the best of our knowledge, a novel framework that leverages facial geometry information to generate more realistic results. Our method comprises a 3D Shape Identity Encoder, which extracts identity and 3D shape features. We incorporate a 3D face reconstruction model to ensure the three-dimensional effect of face makeup, thereby preserving the characters’ depth and natural appearance. To preserve background consistency, our Background Correction Decoder automatically predicts an adaptive mask for the source image, distinguishing the foreground and background. In addition to popular benchmarks, we introduce a new large-scale High Resolution Synthetic Makeup Dataset containing 335,230 diverse high-resolution face images to evaluate our method’s generalization ability. Experiments demonstrate that IP23-Net achieves high-fidelity makeup transfer while effectively preserving background consistency. The code will be made publicly available.

Photoluminescence-Tunable organic phosphine cuprous halides clusters for X-ray scintillators and white light emitting diodes

Low-dimensional cuprous clusters demonstrate fascinating luminescent properties due to their uniquely diversified charge transfer excited states, which imply it could be ideal candidate for multifunctional applications, such as X-ray scintillators and white light emitting diodes (WLED). However, it is still challenge for low-dimensional cuprous clusters that how to regulate the emission performance to meet the requirements of the specific applications. In this work, we prepared a series of luminescent organic cuprous iodide clusters, (IPP)CuI2 and (IPP)2CuI3, which show tunable emission property by simply component regulation. The (IPP)CuI2 with IPP: CuI = 1: 0.75 show the best scintillator property with super-low detection limit of 62.29 nGy/s and high light yields of 75793.83 photons/MeV which is far beyond most organic copper halide scintillators. A series of clear digital radiography (DR) with high spatial resolution of 10.2 lp/mm and a reconstructed 3D model based on computed tomography (CT) were successfully obtained by the prepared scintillation screen. Moreover, the single-component WLED based on accurately component regulation (IPP)CuI2 with IPP: CuI = 1: 0.99 exhibits pure white emission with a high CRI (color rendering index) of 82. The luminescence mechanism of charge transfer excited states in tunable emission (IPP)CuI2 was also investigated. Our works not only provide a kind of ideal materials applied for high-performance single-component WLED and scintillator but also reveal the large potential of low-dimensional organic cuprous halides as luminescent material for multifunctional applications.

Implementation Paper on Automated 2D Image to 3D Model Generation

With the rise of digital world, traditional learning methods, while time-tested, often fall short in meeting the dynamic educational needs of today’s learners. They are constrained by several drawbacks that impede effective comprehension and engagement. This abstract sheds light on the limitations of traditional learning approaches and introduces how the integration of 3D model reconstruction can mitigate these challenges, thereby enhancing the learning experience. We proposed deep Conventional techniques, which create a virtual view from a reference picture, use two distinct steps: depth image-based rendering (DIBR) with estimated depth and depth (or disparity) estimation for the reference image and based on these steps we can create 3D model. Which allows educators to create immersive and interactive learning environments that engage students on a profound level. By converting abstract concepts into tangible and visually stimulating representations, 3D models bridge the gap between theory and practice, making complex subjects more accessible. Keywords: 2D Image, 3D Model, CNN, DIBR

Structured Generation Method of 3D Synthetic Tree Models for Precision Assessment

Abstract. The technology for 3D reconstruction of tree models based on point clouds has been extensively researched, necessitating effective datasets for the study of branch and leaf separation, skeleton point extraction, and tree parameter extraction methods. However, existing datasets for 3D tree models face several challenges, including insufficient data volume for deep learning network training, low accuracy of model ground truth impeding effective method precision evaluation, and a lack of dataset richness to satisfy the needs of multi-type method assessments. In response to these challenges, This paper introduces, for the first time, a fully automated method for generating structured three-dimensional synthetic tree models, and constructs a large-scale 3D synthetic tree dataset enriched with comprehensive structural information. This method facilitates automated computation across several processes, including the mass generation of simulated trees, separation of branches and leaves, noise generation, extraction of skeleton points, and volume calculation. To validate the usability of this dataset across various applications, this paper employs state-of-the-art (SoTA) algorithms to verify the accuracy of methods in 3D tree model reconstruction and carbon stock calculation, thereby thoroughly demonstrating the dataset’s effectiveness.

Evaluating geometric measurement accuracy based on 3D model reconstruction of nursery tomato plants by Agisoft photoscan software

Measuring the vegetation structure of a plant with the help of 3D images is a non-destructive method that can be used to monitor the plant’s status during the growing season. Structure from motion (SfM) is a popular optical distance measurement method that uses 2D images to create 3D models. Identifying factors that influence the precision of SfM is important due to its increasing use in phenotyping. The primary objectives of this study were, i) to scrutinize the impact of imaging parameters—including, color checkerboard location, overlap size of images, and background filtering—on the quality indices of 3D models, and ii) to attain precise measurements of leaf area, stem diameter, and height through the combination of images captured from different camera shooting angles. The three nursery tomato plants were photographed at every five-degree rotation of the turntable, five different locations of color checkerboards, and five camera shooting angles. Results indicated the color checkerboard’s presence and position affect the quality indices of the 3D models. Visually and by the lowest mean keypoint size value, the least noisy 3D model was obtained when the wall of the plant pot was covered with a checkerboard. When the base-to-distance (b/y) ratio as the image overlap index increased from 0.1 to 0.2, the generation time of the 3D models decreased by more than two times. Optimal Mean Absolute Percentage Error (MAPE) values of 1.08 %, 1.01 %, and 1.38 % were achieved for the estimation of leaf area, stem diameter, and height, respectively. This was recorded by using images taken at 0°, 30°, and 105° vertical azimuth angles. Constructing a 3D model by integrating images captured from other angles or more azimuth angles represents numerous visual challenges and leads to problems of point mismatch, which affects the accuracy of measurements.

Automatic Reconstruction of 3D Models from 2D Drawings: A State-of-the-Art Review

Among the methods of 3D reconstruction, the automatic generation of 3D models from building documentation is one of the most accessible and inexpensive. For 30 years, researchers have proposed multiple methods to automatically generate 3D models from 2D drawings. This study compiles this research and discusses the different methods used to generate 3D models from 2D drawings. It offers a critical review of these methods, focusing on the coverage and completeness of the reconstruction process. This review allows us to identify the research gaps in the literature, and opportunities for improvement are identified for future research.

Methods and Applications of Space Understanding in Indoor Environment—A Decade Survey

The demand for digitizing manufacturing and controlling processes has been steadily increasing in recent years. Digitization relies on different techniques and equipment, which produces various data types and further influences the process of space understanding and area recognition. This paper provides an updated view of these data structures and high-level categories of techniques and methods leading to indoor environment segmentation and the discovery of its semantic meaning. To achieve this, we followed the Systematic Literature Review (SLR) methodology and covered a wide range of solutions, from floor plan understanding through 3D model reconstruction and scene recognition to indoor navigation. Based on the obtained SLR results, we identified three different taxonomies (the taxonomy of underlying data type, of performed analysis process, and of accomplished task), which constitute different perspectives we can adopt to study the existing works in the field of space understanding. Our investigations clearly show that the progress of works in this field is accelerating, leading to more sophisticated techniques that rely on multidimensional structures and complex representations, while the processing itself has become focused on artificial intelligence-based methods.

Depth estimation and 3D reconstruction from UAV-borne imagery: Evaluation on the UseGeo dataset

Depth estimation and 3D model reconstruction from aerial imagery is an important task in photogrammetry, remote sensing, and computer vision. To compare the performance of different image-based approaches, this study presents a benchmark for UAV-based aerial imagery using the UseGeo dataset. The contributions include the release of various evaluation routines on GitHub, as well as a comprehensive comparison of baseline approaches, such as methods for offline multi-view 3D reconstruction resulting in point clouds and triangle meshes, online multi-view depth estimation, as well as single-image depth estimation using self-supervised deep learning. With the release of our evaluation routines, we aim to provide a universal protocol for the evaluation of depth estimation and 3D reconstruction methods on the UseGeo dataset. The conducted experiments and analyses show that each method excels in a different category: the depth estimation from COLMAP outperforms that of the other approaches, ACMMP achieves the lowest error and highest completeness for point clouds, while OpenMVS produces triangle meshes with the lowest error. Among the online methods for depth estimation, the approach from the Plane-Sweep Library outperforms the FaSS-MVS approach, while the latter achieves the lowest processing time. And even though the particularly challenging nature of the dataset and the small amount of training data leads to a significantly higher error in the results of the self-supervised single-image depth estimation approach, it outperforms all other approaches in terms of processing time and frame rate. In our evaluation, we have also considered modern learning-based approaches that can be used for image-based 3D reconstruction, such as NeRFs. However, due to the significantly lower quality of the resulting 3D models, we have only included a qualitative comparison between NeRF-based and conventional approaches in the scope of this work.

Pixel-level block classification and crack detection from 3D reconstruction models of masonry structures using convolutional neural networks

Inspection and documentation of masonry structures is a time-consuming and expensive process that heavily relies on an engineer's expertise. This paper introduces a computer vision-based method that automates the creation of classified point clouds from 3D reconstruction models obtained through photogrammetry and/or laser scanning. By leveraging Convolutional Neural Networks (CNN) on 3D renders, the proposed methodology can be used to classify structural features (i.e., blocks, mortar, cracks) with greater accuracy and less effort than conventional methods. Moreover, this approach is flexible and can include further classifications by incorporating additional CNN models, allowing broader applications across various materials (i.e., concrete, steel, timber) and defects. Additionally, a precise methodology for accurate crack quantification featuring a manual annotation tool was introduced which was validated using outputs from a full-scale masonry arch bridge test carried out in the laboratory. The results emphasize the robustness of the classification approach and highlight the useful geometric information that can be gained from full-scale masonry structures. The proposed approach has the potential to use image data from UAVs/static cameras and revolutionize the way we document and inspect structures in the future.

Pulmonary Arterial Anatomical Patterns: A Classification Scheme Based on Lobectomy and 3D-CTBA.

Preoperative evaluation of pulmonary vascular and tracheal routes and variations is of great importance to the surgeon. Three-dimensional computed tomography bronchography and angiography (3D-CTBA) has evolved in recent years with the optimization of 3D reconstruction techniques and artificial intelligence. We aim to apply CT angiography and Exoview 3D reconstruction technology to assess patients' pulmonary arterial tree and its anatomical variants and to try to summarize a set of anatomical typing of the pulmonary arterial tree that is relatively easy and conducive to promoting teaching based on surgical habits of lobectomy. A total of 358 patients hospitalized in the Department of Thoracic Surgery of the First Affiliated Hospital of Soochow University between July 2020 and August 2021 were included in this study. We carefully analyzed the site of emanation, alignment, and number of branches of the pulmonary artery according to a uniform classification method in conjunction with the two-dimensional CT images and transformed them into 3D reconstruction models. Different types of pulmonary artery were observed in 358 cases. We evaluated the complete pulmonary artery tree and counted the number and frequency of major arteries of the pulmonary based on the surgical habits of anatomical lobectomy. The 3D-CTBA technique enables us to adequately assess the anatomy of the pulmonary arteries. Moreover, we provide a practical classification scheme of pulmonary arterial anatomical patterns based on lobectomy and 3D-CTBA. Our data can be used by clinicians in the teaching of pulmonary artery anatomy and the preoperative preparation for anatomical lobectomy.

Fast 3D Reconstruction for Unknown Space Targets via Neural Radiance Field

In this study, a novel residual space object 3D model reconstruction approach is proposed. First, several low-cost CubeSats are assigned to carry out multi-view image capture in the vicinity of the space target, yielding a series of images with relative pose information. Then, a fast 3D reconstruction approach based on NeRF is introduced, in which real-time performance and reconstruction quality are improved for fast on-board 3D reconstruction. Finally, a simulation test is designed to verify the performance of the proposed method, results show that the proposed NeRF-based method is superior to traditional method in terms of reconstruction speed and texture richness.

New methodology for the characterization of 3D model reconstructions to meet conditions of input data and requirements of downstream application

AbstractIn the field of 3D model reconstruction, manifold methods have been developed that derive CAD models from 3D scan data. Opposed to classical CAD modelling, where surface and solid modelling exist, a further diversification of modelling techniques is observed, caused by different methods to build up the geometry. This research introduces a new classification, the so-called Level of Complexities. It can be applied to the complete Reverse Engineering process chain and lays the foundation for further research on how to match requirements arising from all process steps and downstream applications.