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


by K Schauwecker, R Klette, A Zell
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 low-performance embedded platform. The stereo matching results prove to be superior to those obtained with ordinary sparse matching algorithms. © 2012 IEEE.
Reference:
A new feature detector and stereo matching method for accurate high-performance sparse stereo matching (K Schauwecker, R Klette, A Zell), In IEEE International Conference on Intelligent Robots and Systems, 2012.
Bibtex Entry:
@inproceedings{schauwecker2012amatching,
author = "Schauwecker, K and Klette, R and Zell, A",
booktitle = "IEEE International Conference on Intelligent Robots and Systems",
pages = "5171--5176",
title = "A new feature detector and stereo matching method for accurate high-performance sparse stereo matching",
year = "2012",
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 low-performance embedded platform. The stereo matching results prove to be superior to those obtained with ordinary sparse matching algorithms. © 2012 IEEE.",
doi = "10.1109/IROS.2012.6385492",
isbn = "9781467317375",
issn = "2153-0858",
eissn = "2153-0866",
language = "eng",
}