A fast stereo matching algorithm suitable for embedded real-time systems

Humenberger, Martin; Zinner, Christian; Weber, Michael; Kubinger, Wilfried; Vincze, Markus

doi:10.1016/j.cviu.2010.03.012

Cited by 172 publications

(77 citation statements)

References 32 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…For a recent example of a very fast implementation based on this method, with comparisons to other real-time algorithms, see [6]. The reported implementation processes approximately 63 frames per second on a CPU, on test data of 320 × 240 resolution, and for only 16 disparity levels.…”

Section: Related Workmentioning

confidence: 99%

A new feature detector and stereo matching method for accurate high-performance sparse stereo matching

Schauwecker

Klette

Zell

2012

2012 IEEE/RSJ International Conference on Intelligent Robots and Systems

View full text Add to dashboard Cite

Abstract-Hardware platforms with limited processing power are often incapable of running dense stereo analysis algorithms at acceptable speed. Sparse algorithms provide an alternative but generally lack in accuracy. To overcome this predicament, we present an efficient sparse stereo analysis algorithm that applies a dense consistency check, leading to accurate matching results. We further improve matching accuracy by introducing a new feature detector based on FAST, which exhibits a less clustered feature distribution. The new feature detector leads to a superior performance of our stereo analysis algorithm. Performance evaluation shows that the proposed stereo matching system achieves processing rates above 200 frames per second on a commodity dual core CPU, and faster than video frame-rate processing on a lowperformance embedded platform. The stereo matching results prove to be superior to those obtained with ordinary sparse matching algorithms.

show abstract

Section: Related Workmentioning

confidence: 99%

A new feature detector and stereo matching method for accurate high-performance sparse stereo matching

Schauwecker

Klette

Zell

2012

2012 IEEE/RSJ International Conference on Intelligent Robots and Systems

View full text Add to dashboard Cite

show abstract

“…In Humenberger's work (Humenberger et al, 2010), a comparison of prosessing speed of some real-time stereo vision systems has been made. The proposed methods in this chapter are computed on the platform of CPU.…”

Section: Comparison Of Our Methods With Other Methodsmentioning

confidence: 99%

“…The SAD is the most frequently used matching cost function in applications since it is one of the more computationally efficient methods. The advantage of SAD has been mentioned in literatures (Humenberger, 2010;Point Grey Research Inc., 2000;Bradski, 2010). In our application, the computation time is an important factor, therefore the SAD will be the matching cost function for searching the correspondence.…”

Section: Matching Cost Functionmentioning

confidence: 99%

Stereo Matching Method and Height Estimation for Unmanned Helicopter

Hsia¹,

Lien²,

Su³

2012

Machine Vision - Applications and Systems

View full text Add to dashboard Cite

“…The CPU-GPU embedded systems may increase the system performance of existing applications. For example, the stereo matching application for embedded systems has a considerable increase of frame rate processing when is performed onto GPU [7].…”

Section: Cpu-gpu Embedded Systemsmentioning

confidence: 99%

Parallel Execution Optimization of GPU-aware Components in Embedded Systems

Campeanu

2017

International Conferences on Software Engineering and Knowledge Engineering

View full text Add to dashboard Cite

Abstract-Many embedded systems process huge amount of data that comes from the interaction with the environment. The Graphics Processing Unit (GPU) is a modern embedded solution that tackles the efficiency challenge when processing a lot of data. GPU may improve even more the system performance by allowing multiple activities to be executed in a parallel manner. In a complex component-based application, the challenge is to decide the components to be executed in parallel on GPU when considering different system factors (e.g., GPU memory, GPU computation power).In the context of component-based CPU-GPU embedded systems, we propose an automatic method that provides parallel execution schemes of components with GPU capabilities. The introduced method considers hardware (e.g., available GPU memory), software properties (e.g., required GPU memory) and communication pattern. Moreover, the method optimizes the overall system performance based on component execution times and system architecture (i.e., communication pattern). The validation uses an underwater robot example to describe the feasibility of our proposed method.

show abstract

A fast stereo matching algorithm suitable for embedded real-time systems

Cited by 172 publications

References 32 publications

A new feature detector and stereo matching method for accurate high-performance sparse stereo matching

A new feature detector and stereo matching method for accurate high-performance sparse stereo matching

Stereo Matching Method and Height Estimation for Unmanned Helicopter

Parallel Execution Optimization of GPU-aware Components in Embedded Systems

Contact Info

Product

Resources

About