Hierarchical and Multi-Level Cost Aggregation For Stereo Matching

Nowadays, convolutional neural networks based on deep learning have greatly improved the performance of stereo matching. To obtain higher disparity estimation accuracy in ill-posed regions, this paper proposes a hierarchical and multi-level model based on a novel cost aggregation module (HMLNet). This effective cost aggregation consists of two main modules: one is the multi-level cost aggregation which incorporates global context information by fusing information in different levels, and the other called the hourglass+ module utilizes sufficiently volumes in the same level to regularize cost volumes better. Also, we take advantage of disparity refinement with residual learning to boost robustness to challenging situations. We conducted comprehensive experiments on Sceneflow, KITTI 2012, and KITTI 2015 datasets. The competitive results prove that our approach outperforms many other stereo matching algorithms.