1,720,991 research outputs found
Rameshcleaner: Conservative fixing of triangular meshes
No full textIn this work, after a careful examination of the most common errors and flaws which typically occur in meshes produced by 3D scanning processes, we propose a set of fixing tools which solve effectively several important mesh defects while preserving the original data. The proposed tools are then organized and activated in the RameshCleaner pipeline allowing the user to take advantage of a semi-automated fixing solution, optimized for speed and efficiency, as well as of the possibility to selectively activate individual tools. The comparison, over a set of representative scanned models, with free and commercial semi-automated fixing solutions gives a significant evidence of the defect abatement and computational speed characteristics of the proposed system
Increasing tracking robustness for low-complexity real-time reconstructions with handheld optical scanners
Full text linkFast centroidal deformation for large mesh models
No full textWe present an algorithm that allows fast non-linear deformation editing on high-quality meshes. The proposed Fast Centroidal Deformation (FCD) method is based on a multi-resolution framework, where a centroidal deformation graph is built over the mesh in order to allow fast non-linear optimization at a coarse scale. The resulting deformation is then propagated to the initial dense mesh by exploiting the relationship between the constructed deformation graph and the input mesh through a mapping function that unifies local rotations and global translations without the need of solving a system composed by a number of linear equations of the same magnitude of the number of vertices of the mesh. A number of flexible user constraints can be imposed in the deformation through a handle-based metaphor where the user can redefine the position and orientation of single control points or entire portions of the input model. The proposed method addresses the obstacle of non-linear deformation on meshes composed by millions of vertices and is compared with the reference deformation techniques, showing significant improvements in terms of computational efficiency without renouncing to the quality of the results given by non-linear methods
A Computational Model of Custom 3D Printed Hand Orthosis
No full text3D printed patient-specific hand orthoses can improve the efficiency of the treatment and the comfort of the patient, but since each customized orthosis is a virtually unique device, it is difficult to assess their mechanical response in the design phase, both experimentally and numerically. The Finite Element Method (FEM) could be used to predict the deformation of the orthosis under predetermined loads, but patient-specific models including interaction with the hand are still lacking. In the present work we present a computational model in which, starting from the scan data of the hand used to manufacture the orthosis, a FEM model of the hand is generated, including a skeletal structure. Hand bones positions and dimensions can be defined basing on simple anatomical measurements or literature data and the stiffness of the joints can be tuned in relation to patient pathology. The remaining hand volume consists of a soft tissue region, mimicking the non-linear mechanical behaviour of skin and muscles. Results show that both functional and structural indexes can be analyzed, such as contact pressures, stress state or the compliance of the orthosis, providing useful information for the design of custom devices. By using mesh deformation algorithms, the scan data could be used to generate different orthosis designs in target positions defined by the therapist and, taking advantage of a parametric model under development, the skeletal structure could be adapted correspondingly, providing an innovative pathway to investigate the response of the orthosis during the whole rehabilitation
DenseMatch: a dataset for real-time 3D reconstruction
Full text linkWe provide a database aimed at real-time quantitative analysis of 3D reconstruction and alignment methods, containing 3140 point clouds from 10 subjects/objects. These scenes are acquired with a high-resolution 3D scanner. It contains depth maps that produce point clouds with more than 500k points on average. This dataset is useful to develop new models and alignment strategies to automatically reconstruct 3D scenes from data acquired with optical scanners or benchmarking purposes
Design of a Visual Environment for Evaluating and Customizing Medical Image Compression Techniques
No full textBimodal ECG-PCG Cardiovascular Disease Detection: a Close Look at Transfer Learning and Data Collection Issues
Full text linkEarly detection of cardiovascular diseases (CVDs) is crucial for minimizing their adverse impact on patients' health. Electrocardiograms (ECGs), which capture the heart's electrical activity, have been widely used to primarily evaluate heart conduction disorders. On the other hand, phonocardiograms (PCGs) recorded during cardiac auscultation, have been less explored, often being overlooked in favor of echocardiograms for detecting mechanical issues such as valvular diseases. However, due to their low cost and non-invasive nature, the analysis of both ECGs and PCGs can be easily integrated into preventive settings. Combining effectively the complementary information from these two modalities could significantly enhance the early detection of CVDs, where Machine Learning (ML) techniques can offer promising and cost-effective solutions. Progress in this area, however, has been limited by the lack of large enough datasets containing both ECG and PCG signals. One objective of this work is to analyze in-depth prior bimodal CVD detection research, identifying key issues to better address data collection and transfer learning limitations. We also propose a different approach to transfer learning for improving heart sound interpretation. Our findings confirm the effectiveness of using both signals to detect abnormal heart conditions. However, we also notice that even a refined transfer learning approach to enhance PCG interpretation is not enough to fully address the issues coming from the lack of bimodal data, indicating the need for further efforts in this direction. Ultimately, our bimodal approach achieved an overall AUROC of 96.4%, exceeding the performance of corresponding ECG-only and PCG-only models by approximately 3% and 10%, respectively. Compared to the other existing approaches, our method demonstrated superior AUROC performance while maintaining a relatively low false-negative rate, which is critical in CVD screening contexts
3DReg-i-Net: Improving deep learning based 3D registration for a robust real-time alignment of small-scale scans
No full textWe present 3DReg-i-Net, an improved deep learning solution for pairwise registration of 3D scans, which evolves the recently proposed 3DRegNet technique by Pais et al. This is one of the very first learning based algorithm aiming at producing the co-registration of two 3D views starting solely from a set of point correspondences, which is able to perform outlier rejection and to recover the registration matrix. We evolve the original method to face the challenging scenario of quick 3D modelling at small scales through the alignment of dense 3D views acquired at video frame-rate with a handheld scanner. We improve the system tracking robustness and alignment performance with a generalized input data augmentation. Moreover, working on suboptimal aspects of the original solution, we propose different improvements that lead to a redefinition of the training loss function. When tested on the considered scenario, the proposed 3DReg-i-Net significantly outperforms the prior solution in terms of accuracy of the estimated aligning transforms
Going Beyond Counting First Authors in Author Co-citation Analysis
Full text linkThe present study examines one of the fundamental aspects of author co-citation analysis (ACA) - the way co-citation
counts are defined. Co-citation counting provides the data on which all subsequent statistical analyses and mappings
are based, and we compare ACA results based on two different types of co-citation counting - the traditional type that
only counts the first one among a cited work's authors on the one hand and a non-traditional type that takes into
account the first 5 authors of a cited work on the other hand. Results indicate that the picture produced through this non-traditional author co-citation counting contains more coherent author groups and is therefore considerably clearer. However, this picture represents fewer specialties in the research field being studied than that produced through the traditional first-author co-citation counting when the same number of top-ranked authors is selected and analyzed. Reasons for these effects are discussed
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