Large steps in cloth simulation
Proceedings of the 25th annual conference on Computer graphics and interactive techniques
Surfels: surface elements as rendering primitives
Proceedings of the 27th annual conference on Computer graphics and interactive techniques
Point based animation of elastic, plastic and melting objects
SCA '04 Proceedings of the 2004 ACM SIGGRAPH/Eurographics symposium on Computer animation
Meshless deformations based on shape matching
ACM SIGGRAPH 2005 Papers
A performance-oriented data parallel virtual machine for GPUs
ACM SIGGRAPH 2006 Sketches
A performance study of general-purpose applications on graphics processors using CUDA
Journal of Parallel and Distributed Computing
ISBMS '08 Proceedings of the 4th international symposium on Biomedical Simulation
Exploring Parallel Algorithms for Volumetric Mass-Spring-Damper Models in CUDA
ISBMS '08 Proceedings of the 4th international symposium on Biomedical Simulation
Meshfree Particle Methods
A point-based method for animating elastoplastic solids
Proceedings of the 2009 ACM SIGGRAPH/Eurographics Symposium on Computer Animation
Real-Time simulation of deformable soft tissue based on mass-spring and medial representation
CVBIA'05 Proceedings of the First international conference on Computer Vision for Biomedical Image Applications
Hi-index | 0.00 |
We have developed a framework that uses multicore CPUs and GPUs found on personal computers to accelerate the computations needed for a class of deformable object modeling algorithms. In recent years there has been a growing interest in using deformable objects in computer applications such as animation, video games, garment CAD, and surgical simulation. Deformable object modeling is quite expensive computationally. However, since most of the related calculations can be parallelized, we have developed a framework that utilizes NVIDIA's CUDA technology to accelerate a set of deformable object modeling algorithms by transferring their core computations to the GPU. Our results show that frame rates can be improved more than 20 times using GPU compared with using a multicore CPU. In addition, we have developed a method called Local Shape Matching which is an extension to the Shape Matching method. Using this new method we have achieved fast and robust simulations whose implementations have good numerical stability.