Shuffle in 3D: A Lightweight Architecture for Stereo Matching - Xiao, Jianqiang; Yamane, Satoshi (2021 IEEE 10th Global Conference on Consumer Electronics (GCCE), 2021)
The deep learning-based stereo matching approaches commonly construct 3D cost volume with a Siamese network, and the 3D encoder-decoder architectures regularize 3D cost volume to generate disparity as output. Yet, the 3D encoder-decoder architectures produce tremendous parameters that causing huge training and inference cost. In this paper, we propose a 3D ShuffleNet-based approach on PSMNet, which greatly saves the computational cost without losing without reducing the performance of the network.