Computer aided design (CAD) models are widely employed in the current computer aided engineering or finite element analysis (FEA) systems that necessitate an optimal meshing as a function of their geometry. To this effect, the sub-mapping method is advantageous, as it segments the CAD model into different sub-parts, with the aim mesh them independently. Many of the existing 3D shape segmentation methods in literature are not suited to CAD models. Therefore, we propose a novel approach for the segmentation of CAD models by harnessing deep learning technologies. First, we refined the model and extracted local geometric features from its shape. Subsequently, we devised a convolutional neural network (CNN)-inspired neural network trained with a custom dataset. Experimental results demonstrate the robustness of our approach and its potential to adapt to augmented datasets in future.
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CAD Model Segmentation Via Deep Learning
Semantic Scholar · Computer Science · 2021
Abstract
Computer aided design (CAD) models are widely employed in the current computer aided engineering or finite element analysis (FEA) systems that necessitate an optimal meshing as a function of their geometry. To this effect, the sub-mapping method is advantageous, as it segments the CAD model into different sub-parts, with the aim mesh them independently. Many of the existing 3D shape segmentation methods in literature are not suited to CAD models. Therefore, we propose a novel approach for the segmentation of CAD models by harnessing deep learning technologies. First, we refined the model and extracted local geometric features from its shape. Subsequently, we devised a convolutional neural network (CNN)-inspired neural network trained with a custom dataset. Experimental results demonstrate the robustness of our approach and its potential to adapt to augmented datasets in future.