Feature Curve Extraction on Triangle Meshes

Thompson, Elia Moscoso; Arvanitis, Gerasimos; Μουστάκας, Κωνσταντίνος; Hoang-Xuan, Nhat; Nguyen, E-Ro; Tran, Minh-Triet; Lejemble, Thibault; Barthe, Loïc; Mellado, Nicolas; Romanengo, Chiara; Biasotti, Silvia; Falcidieno, Bianca

doi:10.2312/3dor.20191066

Cited by 1 publication

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“…SHREC dataset. We use the dataset produced for feature curve extraction by Thompson et al [2019] to evaluate the performance of our network on challenging data annotated by humans. As for ABC, we consider the vertices and normal vectors of the given meshes and mark the annotated vertices as edge vertices.…”

Section: Experimental Setup 41 Point Cloud Datasetmentioning

confidence: 99%

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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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Section: Experimental Setup 41 Point Cloud Datasetmentioning

confidence: 99%

“…This generates contextualized and potentially ambiguous interpretation of what edges are. For instance, different persons produced different annotations on the models provided for the recent feature curve detection contest [Thompson et al 2019]. These models include a large variety of different edges, from sharp to smooth and rounded.…”

Section: Introductionmentioning

confidence: 99%