A versatile stereo implementation on commodity graphics hardware

Yang, Ruigang; Pollefeys, Marc

doi:10.1016/j.rti.2005.04.002

Cited by 48 publications

(31 citation statements)

References 24 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…However, they focused on the collaboration of CPU and GPU, instead of fully utilizing resources on the GPU. Yang and Pollefeys [12] proposed GPU-based real-time stereo matching with multiple images. They implemented rectification and disparity computing using standard use of texture.…”

Section: Image Processing On the Legacy Gpgpu Platformmentioning

confidence: 99%

Design and Performance Evaluation of Image Processing Algorithms on GPUs

Park

Singhal

Lee

et al. 2011

IEEE Trans. Parallel Distrib. Syst.

124

View full text Add to dashboard Cite

Abstract-In this paper, we construe key factors in design and evaluation of image processing algorithms on the massive parallel graphics processing units (GPUs) using the compute unified device architecture (CUDA) programming model. A set of metrics, customized for image processing, is proposed to quantitatively evaluate algorithm characteristics. In addition, we show that a range of image processing algorithms map readily to CUDA using multiview stereo matching, linear feature extraction, JPEG2000 image encoding, and nonphotorealistic rendering (NPR) as our example applications. The algorithms are carefully selected from major domains of image processing, so they inherently contain a variety of subalgorithms with diverse characteristics when implemented on the GPU. Performance is evaluated in terms of execution time and is compared to the fastest host-only version implemented using OpenMP. It is shown that the observed speedup varies extensively depending on the characteristics of each algorithm. Intensive analysis is conducted to show the appropriateness of the proposed metrics in predicting the effectiveness of an application for parallel implementation.

show abstract

Section: Image Processing On the Legacy Gpgpu Platformmentioning

confidence: 99%

Design and Performance Evaluation of Image Processing Algorithms on GPUs

Park

Singhal

Lee

et al. 2011

IEEE Trans. Parallel Distrib. Syst.

124

View full text Add to dashboard Cite

show abstract

“…The wide deployment of GPU in the last several years has resulted in a widely range of applications implemented on a graphics processor such as physics simulations [26][27], signal and image processing [28][29], computer vision [30], global illumination [31], geometric computing [32] or databases and data mining [33].…”

Section: Previous Workmentioning

confidence: 99%

The 2D wavelet transform on emerging architectures: GPUs and multicores

Franco

Bernabé

Fernández

et al. 2011

J Real-Time Image Proc

View full text Add to dashboard Cite

We present in this paper several implementations of the 3D Fast Wavelet Transform (3D-FWT) on multicore CPUs and manycore GPUs. On the GPU side, we focus on CUDA and OpenCL programming to develop methods for an efficient mapping on manycores. On multicore CPUs, OpenMP and Pthreads are used as counterparts to maximize parallelism, and renowned techniques like tiling and blocking are exploited to optimize the use of memory. We evaluate these proposals and make a comparison between a new Fermi Tesla C2050 and an Intel Core 2 Quad Q6700. Speedups of the CUDA version are the best results, improving the execution times on CPU, ranging from 5.3x to 7.4x for different image sizes, and up to 81 times faster when communications are neglected. Meanwhile, OpenCL obtains solid gains which range from 2x factors on small frame sizes to 3x factors on larger ones.

show abstract

“…And the original algorithm is constructed via a iterative and sequential procedure which is difficult to utilize hardware parallelism. In order to achieve real-time, recent advances exploit the parallel computational power within GPUs [12,13]. With some necessary modifications, some global methods also implemented in the very large scale integration circuit [14,15] at the expense of considerable high consumption of logic source, memory and bandwidth.…”

Section: Background and Related Workmentioning

confidence: 99%

Real-time stereo vision system using adaptive weight cost aggregation approach

Ding

Liu

Zhou

et al. 2011

J Image Video Proc.

View full text Add to dashboard Cite

Many vision applications require high-accuracy dense disparity maps in real time. Due to the complexity of the matching process, most real-time stereo applications rely on local algorithms in the disparity computation. These local algorithms generally suffer from matching ambiguities as it is difficult to find appropriate support for each pixel. Recent research shows that algorithms using adaptive cost aggregation approach greatly improve the quality of disparity map. Unfortunately, although these improvements are excellent, they are obtained at the expense of high computational. This article presents a hardware implementation for speeding up these methods. With hardware friendly approximation, we demonstrate the feasibility of implementing this expensive computational task on hardware to achieve real-time performance. The entire stereo vision system, includes rectification, stereo matching, and disparity refinement, is realized using a single field programmable gate array. The highly parallelized pipeline structure makes system be capable to achieve 51 frames per second for 640 × 480 stereo images. Finally, the success of accuracy improvement is demonstrated on the Middlebury dataset, as well as tests on real scene.

show abstract

A versatile stereo implementation on commodity graphics hardware

Cited by 48 publications

References 24 publications

Design and Performance Evaluation of Image Processing Algorithms on GPUs

Design and Performance Evaluation of Image Processing Algorithms on GPUs

The 2D wavelet transform on emerging architectures: GPUs and multicores

Real-time stereo vision system using adaptive weight cost aggregation approach

Contact Info

Product

Resources

About