Few-Shot Stereo Matching with High Domain Adaptability Based on Adaptive Recursive Network

Abstract

Deep learning based stereo matching algorithms have been extensively researched in areas such as robot vision and autonomous driving due to their promising performance. However, these algorithms require a large amount of labeled data for training and encounter inadequate domain adaptability, which degraded their applicability and flexibility. This work addresses the two deficiencies and proposes a few-shot trained stereo matching model with high domain adaptability. In the model, stereo matching is formulated as the problem of dynamic optimization in the possible solution space, and a multi-scale matching cost computation method is proposed to obtain the possible solution space for the application scenes. Moreover, an adaptive recurrent 3D convolutional neural network is designed to determine the optimal solution from the possible solution space. Experimental results demonstrate that the proposed model outperforms the state-of-the-art stereo matching algorithms in terms of training requirements and domain adaptability.