Noriaki Shirai

Real-time stereo vision system at nighttime with noise reduction using simplified non-local matching cost

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.

A real-time dense stereo matching method for critical environment sensing in autonomous driving

This paper proposes a novel real-time stereo matching algorithm paired with an online auto-rectification framework to solve environment sensing problems in autonomous driving, such as road, curb and small object detection etc. The algorithm treats disparities of stereo images as hidden states and conducts a Viterbi process at 4 bi-directional paths to estimate them. Structural similarity, total variation constraint, and a specific hierarchical merging strategy are combined with the Viterbi process to improve the robustness and accuracy. Based on the results of Viterbi, a convex optimization equation is derived to estimate epipolar line distortion. The estimated distortion information is used for the online compensation of Viterbi process at an auto-rectification framework. The stereo matching result is used to generate the histogram map. A Radon transform and a Viterbi process are applied to the histogram map to detect roads and curbs. Disparities near the road are mapped to 3D space to detect small objects on road. Real-word experiments show that this method can help sensing the surrounding 3D environment for driving vehicles robustly.

Accurate 3D Reconstruction from Naturally Swaying Cameras

In this paper, we propose a method for reconstructing 3D structure accurately from images taken by unintentionally swaying cameras. In this method, image super-resolution and 3D reconstruction are achieved simultaneously by using series of motion blur images. In addition, we utilize coded exposure in order to achieve stable super resolution. Furthermore, we show efficient stereo camera arrangement for stable 3D reconstruction from swaying cameras. The experimental results show that the proposed method can reconstruct 3D shape very accurately.