Architecture for Vertex Transformation and Triangle Clipping in 3D Graphics

Liu, Bo Zhi; Sun, Tao; Zhou, Li; Jia, Weiping; Zhang, Yuan Zhi

doi:10.4028/www.scientific.net/amm.462-463.1040

Cited by 1 publication

(1 citation statement)

References 10 publications

(16 reference statements)

Supporting

Mentioning

Contrasting

Order By: Relevance

“…In graphic processing unit (GPU) implementation, Liu et al (2014) and Alshakargy (2016) implemented 2D rotation for images; they have put more emphasis on affine rotation only, and on another hand, Biswal et al (2013) performed various transformations on 2D images. Nowadays, a lot of work is being done to implement parallel technique into existing algorithms to enhance their performance using GPU rather than FPGAs; matrix multiplication is a computational problem that has been accelerated using GPU as in Taghiyev and Akcay (2013) and Malaya et al (2017) or even multi-core CPU (MC CPU;Michailidis and Margaritis, 2010).…”

Section: Introductionmentioning

confidence: 99%

The acceleration of 3D graphics transformations based on CUDA

Alrawy

Ali

2018

JEDT

View full text Add to dashboard Cite

Purpose The purpose of this paper is to achieve the acceleration of 3D object transformation using parallel techniques such as multi-core central processing unit (MC CPU) or graphic processing unit (GPU) or even both. Generating 3D animation scenes in computer graphics requires applying a 3D transformation on the vertices of the objects. These transformations consume most of the execution time. Hence, for high-speed graphic systems, acceleration of vertex transform is very much sought for because it requires many matrix operations (need) to be performed in a real time, so the execution time is essential for such processing. Design/methodology/approach In this paper, the acceleration of 3D object transformation is achieved using parallel techniques such as MC CPU or GPU or even both. Multiple geometric transformations are concatenated together at a time in any order in an interactive manner. Findings The performance results are presented for a number of 3D objects with paralleled implementations of the affine transform on the NVIDIA GPU series. The maximum execution time was about 0.508 s to transform 100 million vertices using LabVIEW and 0.096 s using Visual Studio. Other results also showed the significant speed-up compared to CPU, MC CPU and other previous work computations for the same object complexity. Originality/value The high-speed execution of 3D models is essential in many applications such as medical imaging, 3D games and robotics.

show abstract

Section: Introductionmentioning

confidence: 99%