Vlad-Cristian Miclea

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Most of the stereo-matching algorithms nowadays need high accuracy, especially for objects at large distances. Lots of approaches are able to provide good results at low costs, but at large distances (small disparities) suffer from the so called “pixellocking effect” i.e. an uneven sub-pixel disparity distribution. In order to compensate this(More)
The accuracy of stereo matching algorithms is one of the key aspects in autonomous driving nowadays. In case of large distances, sub-pixel accurate solutions are required, especially for algorithms in discrete settings. It has been previously shown that a strong correlation between the matching algorithm and the sub-pixel interpolation method exists, and(More)
Recent years have shown a great progress in self-driving vehicles and stereovision has proven to be a key aspect towards this goal. Semi-Global Matching (SGM) algorithm is among the best stereo solutions, capable of producing reliable results at reasonable cost. Census transform is generally preferred as a cost metric due to its robustness and invariance to(More)
Lately stereo matching has become a key aspect in autonomous driving, providing highly accurate solutions at relatively low cost. Top approaches on state of the art benchmarks rely on learning mechanisms such as convolutional neural networks (ConvNets) to boost matching accuracy. We propose a new real-time stereo reconstruction method that uses a ConvNet(More)
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