Hierarchical Neural Architecture Search for Deep Stereo Matching (with Xuelian Cheng, Yiran Zhong, Mehrtash Harandi, Yuchao Dai, Xiaojun Chang, Hongdong Li and Zongyuan Ge)

This paper shows how to apply neural architecture search to the structured problem of stereo matching, where the network has to both extract features and compare them across two views. Rather than searching at a single level, the method searches simultaneously over cell structure (the local feature operations) and network structure (how the cells are connected for matching and aggregation), producing architectures that outperform hand-designed stereo networks like PSMNet on KITTI and Middlebury while using far fewer parameters.

[NeurIPS 2020 paper]