V.C. Miclea, S. Nedevschi
Proceedings of 2017 IEEE Intelligent Vehicles Symposium (IV 17), Los Angeles, CA, USA, 11-14 June 2017, pp. 1795-1802
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. In a ”divide and conquer” approach this segmentation enables us to split the large heterogeneous trafﬁc scene into smaller regions with similar features. We use the segmentation results to enhance Census Transform with an optimal census mask and the SGM energy optimization step with an optimal P1 penalty for each predicted class. Additionally, we improve the sub-pixel accuracy of the stereo matching by ﬁnding optimal interpolation functions for each particular segment class. In both cases we propose new stochastic optimization methods based on genetic algorithms that can incrementally adjust the parameters for better solutions. Tests performed on Kitti and real trafﬁc scenarios show that our method outperforms the accuracy of previous solutions.