MeshLab: an Open-Source Mesh Processing Tool

Cignoni, Paolo; Callieri, Marco; Corsini, Massimiliano; Dellepiane, Matteo; Ganovelli, Fabio; Ranzuglia, Guido

doi:10.2312/localchapterevents/italchap/italianchapconf2008/129-136

Cited by 401 publications

(93 citation statements)

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“…Additionally, close to the ears we often found mesh irregularities such as large edge lengths. The open source tool Meshlab (ISTI-CNR, Pisa, Italy) [12] was used to remove isolated parts, duplicated vertices, and to close holes. After artifacts removal we used isotropic explicit remeshing [48] to avoid large edge lengths and to obtain a regular, uniform surface scan.…”

Section: Dataset and Preprocessingmentioning

confidence: 99%

A statistical shape model for radiation-free assessment and classification of craniosynostosis

Schaufelberger¹,

Kühle²,

Wachter³

et al. 2022

Preprint

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The assessment of craniofacial deformities requires patient data which is sparsely available. Statistical shape models provide realistic and synthetic data enabling comparisons of existing methods on a common dataset.We build the first publicly available statistical 3D head model of craniosynostosis patients and the first model focusing on infants younger than 1.5 years. For correspondence establishment, we test and evaluate four template morphing approaches. We further present an original, shape-modelbased classification approach for craniosynostosis on photogrammetric surface scans. To the best of our knowledge, our study uses the largest dataset of craniosynostosis patients in a classification study for craniosynostosis and statistical shape modeling to date.We demonstrate that our shape model performs similar to other statistical shape models of the human head. Craniosynostosis-specific pathologies are represented in the first eigenmodes of the model. Regarding the automatic classification of craniosynostis, our classification approach yields an accuracy of 97.3 %, comparable to other state-of-the-art methods using both computed tomography scans and stereophotogrammetry.Our publicly available, craniosynostosis-specific statistical shape model enables the assessment of craniosynostosis on realistic and synthetic data. We further present a state-of-the-art shape-modelbased classification approach for a radiation-free diagnosis of craniosynostosis.

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Section: Dataset and Preprocessingmentioning

confidence: 99%

A statistical shape model for radiation-free assessment and classification of craniosynostosis

Schaufelberger¹,

Kühle²,

Wachter³

et al. 2022

Preprint

View full text Add to dashboard Cite

show abstract

“…The simplification scheme (Figure 3B) was defined by performing four iterations of quadric edge collapse on the template in MeshLab software. 25 The four spiral convolutional layers consist of 16, 32, 64, and 128 learned filters, respectively. Afterwards, two fully connected layers are added to compress the data even further to the desired number of scores.…”

Section: Autoencodermentioning

confidence: 99%

Exploring palatal and dental shape variation with 3D shape analysis and geometric deep learning

Nauwelaers

Matthews

Fan

et al. 2021

Orthod Craniofacial Res

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Objectives: Palatal shape contains a lot of information that is of clinical interest. Moreover, palatal shape analysis can be used to guide or evaluate orthodontic treatments. A statistical shape model (SSM) is a tool that, by means of dimensionality reduction, aims at compactly modeling the variance of complex shapes for efficient analysis. In this report, we evaluate several competing approaches to constructing SSMs for the human palate. Setting and Sample Population:This study used a sample comprising digitized 3D maxillary dental casts from 1,324 individuals. Materials and methods: Principal component analysis (PCA) and autoencoders (AE)are popular approaches to construct SSMs. PCA is a dimension reduction technique that provides a compact description of shapes by uncorrelated variables. AEs are situated in the field of deep learning and provide a non-linear framework for dimension reduction. This work introduces the singular autoencoder (SAE), a hybrid approach that combines the most important properties of PCA and AEs. We assess the performance of the SAE using standard evaluation tools for SSMs, including accuracy, generalization, and specificity.

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“…Normal estimation. The normals used for the GLS computation are either directly those estimated with the acquisition technique (e.g., photogrammetry, laser scanner), which are actually provided for most current acquisition devices, or those computed automatically from the point samples (we used Meshlab [Cignoni et al 2008] to estimate normals on Default dataset and on Euler, Lans, Church, and Train Station). We did not encounter any difficulty related to normal estimation.…”

Section: Experimental Setup 41 Point Cloud Datasetmentioning

confidence: 99%

PCEDNet: A Lightweight Neural Network for Fast and Interactive Edge Detection in 3D Point Clouds

et al. 2021

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In recent years, Convolutional Neural Networks (CNN) have proven to be efficient analysis tools for processing point clouds, e.g., for reconstruction, segmentation, and classification. In this article, we focus on the classification of edges in point clouds, where both edges and their surrounding are described. We propose a new parameterization adding to each point a set of differential information on its surrounding shape reconstructed at different scales. These parameters, stored in a Scale-Space Matrix (SSM) , provide a well-suited information from which an adequate neural network can learn the description of edges and use it to efficiently detect them in acquired point clouds. After successfully applying a multi-scale CNN on SSMs for the efficient classification of edges and their neighborhood, we propose a new lightweight neural network architecture outperforming the CNN in learning time, processing time, and classification capabilities. Our architecture is compact, requires small learning sets, is very fast to train, and classifies millions of points in seconds.

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MeshLab: an Open-Source Mesh Processing Tool

Cited by 401 publications

References 0 publications

A statistical shape model for radiation-free assessment and classification of craniosynostosis

A statistical shape model for radiation-free assessment and classification of craniosynostosis

Exploring palatal and dental shape variation with 3D shape analysis and geometric deep learning

PCEDNet: A Lightweight Neural Network for Fast and Interactive Edge Detection in 3D Point Clouds

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