Stereo Matching Network Based on Multi-Scale Information Guided Cost Volume Pyramid Aggregation
To solve the problem of insufficient utilization of multi-scale information in the existing stereo matc-hing networks based on a 4D cost volume pyramid.A stereo matching network based on multi-scale information guided cost volume pyramid aggregation is proposed,this network utilizes multi-dimensional attention mechanisms to optimize the cost volume pyramid aggregation module to improve the utilization of multi-scale information.The network uses the initial prediction of the cost volume pyramid as the attention of the disparity dimension to filter the cost volume and designs a guided hourglass architecture that uses multi-scale feature maps and the global spatial information in cost volume to generate attention for cost volume on the channel dimension.The above methods improve the utilization of multi-scale information by enhancing the interaction between different scales of information,thus improving the network's ability to discern important information.The experimental results show that the proposed network achieves a good balance between inference speed and matching accuracy on SceneFlow,KITTI 2012 and KITTI 2015 datasets,and reduces the number of parameters by 31％and 57％com-pared to CFNet and PCWNet,respectively.
万方数据
维普
