Some of common methods in fabric simulation are compared to each other. This paper reviews and compares presented mesh types in mass spring model, forces applied on model, super elastic effect and ways to settle the super elasticity problem, numerical integration methods for solving equations, collision detection and its response. Also in this paper some of the common methods in process of fabric simulation in mass spring model are discussed and compared. The purpose of this paper is to conduct a survey on research in fabric and cloth simulation using mass spring model. An evaluation of several image segmentation methods trained on our synthetic dataset shows that our approach to new data generation can boost the segmentation accuracy and the generalizabil-ity of the machine learning models to unseen drawings. The effectiveness of our method is demonstrated in the context of a binary component segmentation task with a proposed list of descriptors. Our method is based on the ran-domization of the dimension sets subject to two major constraints to ensure the validity of the synthetic drawings. As one step toward this challenge, we propose a constrained data synthesis method to generate an arbitrarily large set of synthetic training drawings using only a handful of labeled examples. Although recent advances in trainable computer vision methods may enable automatic machine interpretation, it remains challenging to apply such methods to engineering drawings due to a lack of labeled training data.
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While such drawings are a common medium for clients to encode design and manufacturing requirements, a lack of computer support to automatically interpret these drawings necessitates part manufacturers to resort to laborious manual approaches for interpretation which, in turn, severely limits processing capacity.
We present a new data generation method to facilitate an automatic machine interpretation of 2D engineering part drawings.
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