ImageBased EndtoEnd Neural Network for Dense Disparity Estimation Stereo matching is a challenging yet important task to various computer vision applications, e.g. 3D reconstruction, augmented reality, and autonomous vehicles. In this paper, we present a novel imagebased convolutional neural network (CNN) for dense disparity estimation using stereo image pairs. In order to achieve precise and robust stereo matching, we introduce a feature extraction module that learns both local and global information. These features are then passed through an hourglass structure to generate disparity maps from lower resolution to full resolution. We test the proposed method in several datasets including indoor scenes and synthetic scenes. Experimental results demonstrate that the proposed method outperforms the stateoftheart methods in several datasets.

A 3D Convolutional Neural Network for Light Field Depth Estimation Depth estimation has always been a great challenge in the field of computer vision and machine learning. There is a rich literature focusing on depth estimation in stereo vision or in monocular imaging, while the domain of depth estimation in light field images is still in its infancy. The paper proposes a fully convolutional 3D neural network that estimates the disparity in light field images. The proposed method is parametric as it is able to adapt to input images of arbitrary size and it is lightweight and less prone to overfitting thanks to its fully convolutional nature. The experiments reveal competitive results against the state of the art, demonstrating the potential offered by deep learning solutions for disparity estimation in light field images.