Subdivision surface techniques smoothen the surface of any 3D object by splitting the polygons into smaller sub-polygons. However, most methods of subdivision encounter the same problem when dealing with extraordinary points. This project aims is to implement an enhanced Catmull-Clark subdivision scheme and simulated cloth that can detect and identify the collision ofan object against the simulated cloth in a virtual environment. The original Catmull-Clark subdivision scheme was enhanced by manipulating the weights present in the original scheme while adhering to a few rules. The cloth used a mass-spring model to be initialised, and the enhanced subdivision scheme was integrated into this model. Then, the collision detection was performed based on the bounding volume approach, and an appropriate collision response was used to simulate the behaviour of the cloth in real life. Experiments and tests were conducted to evaluate the smoothness ofthe enhanced subdivision scheme and the computation time. The enhanced subdivision scheme was only able to create an acceptably smooth surface until the second iteration ofthe subdivision. On the third iteration, noticeable sharp points were present, which indicated that the enhanced subdivision scheme did not improve the original scheme. Additionally, the execution time for the enhanced subdivision schemewas insignificantly longer compared to the original scheme for all the levels ofsubdivision. The frame rate test showed that the cloth simulation ran at the average rate of43.572 fps, which was within the acceptable range. In conclusion, this research focuses on creating a cloth simulation that implemented an enhanced Catmull-Clark subdivision scheme and collision detection. However, the proposed enhancement for this scheme can be improved to account for the subdivision at individual cases of extraordinary points.
Detection of partially occluded faces in digital images using AdaBoost Haar cascade classifier is a viable technique of face detection if the cascade training procedure is modified.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.
customersupport@researchsolutions.com
10624 S. Eastern Ave., Ste. A-614
Henderson, NV 89052, USA
This site is protected by reCAPTCHA and the Google Privacy Policy and Terms of Service apply.
Copyright © 2024 scite LLC. All rights reserved.
Made with 💙 for researchers
Part of the Research Solutions Family.