MABNet: A Lightweight Stereo Network Based on Multibranch Adjustable Bottleneck Module

Jiabin Xing, Zhi Qi, Jiying Dong, Jiaxuan Cai, Hao Liu ;

Recently, end-to-end CNNs have presented remarkable performance for disparity estimation. But most of them are too heavy to resource-constrained devices, because of enormous parameters necessary for satisfactory results. To address the issue, we propose two compact stereo networks, MABNet and its light version MABNet_tiny. MABNet is based on a novel Multibranch Adjustable Bottleneck (MAB) module, which is less demanding on parameters and computation. In a MAB module, feature map is split into various parallel branches, where the depthwise separable convolutions with different dilation rates extract features with multiple receptive fields however at an affordable computational budget. Besides, the number of channels in each branch is adjustable independently to tradeoff computation and accuracy. On SceneFlow and KITTI datasets, our MABNet achieves competitive accuracy with fewer parameters of 1.65M. Especially, MABNet_tiny reduces the parameters to 47K by cutting down the channels and layers in MABNet."