Real-time stereo vision system at nighttime with noise reduction using simplified non-local matching cost
Yuquan Xu, Qian Long, Seiichi Mita, Hossein Tehrani, Kazuhisa Ishimaru, Noriaki Shirai · 2016
Reconstructing the depth information from the 3D scene using stereo vision is a key element in the development of advanced driver assistance systems. We previously proposed a novel real-time stereo matching method based on the Multi-paths Viterbi that outperforms the well-known SGBM (Semi-Global Block-Matching Algorithm) algorithm in both disparity accuracy and density. In this paper, we extend the previous framework to estimate the depth information for challenging environments such as nighttime. Estimating the depth at nighttime is generally challenging as the night images are dark and noisy and the estimated depth information is not accurate. In our proposed work, we modify the non-local means filter and propose a new non-local cost function to combine the noise reduction and stereo vision within a single framework. We evaluate our proposed algorithm on both natural and synthetic datasets and show that the proposed algorithm can significantly improve the quality of the stereo results in the low light condition. Moreover, our proposed method can be implemented in real-time for autonomous driver applications.