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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 for semantically segmenting the driving scene...
The increase in computational power of consumer graphic cards has successfully motivated adaptation of stereo algorithms to this kind of hardware. In order to solve the stereo correspondence problem efficiently, the images need to be rectified and lens distortions need to be removed. This paper presents an efficient two step solution for rectifying and correcting lens distortions in images captured...
GPU hardware architectures have evolved into a suitable platform for the hardware acceleration of complex computing tasks. Stereo vision is one such task where acceleration is desirable for robotic and automotive systems. Much research was invested in developing stereo vision algorithms with increased quality, but real-time implementations are still lacking. In this work we focus on creating a real-time...
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