An efficient and accurate multi-level cascaded recurrent network for stereo matching

An efficient and accurate multi-level cascaded recurrent network for stereo matching

Blog Article

Abstract With the advent of Transformer-based convolutional neural networks, stereo matching algorithms have achieved state-of-the-art accuracy in disparity estimation.Nevertheless, this method requires much model inference time, which is the main reason limiting its application in many vision tasks and robots.Facing edible bra and panty set the trade-off problem between accuracy and efficiency, this paper proposes an efficient and accurate multi-level cascaded recurrent network, LMCR-Stereo.

To recover the detailed information of stereo images more accurately, we first design a multi-level network to update the difference values in a coarse-to-fine recurrent iterative manner.Then, we propose a new pair of slow-fast multi-stage superposition inference structures to accommodate the differences between different scene data.Besides, to ensure better disparity estimation accuracy with faster model inference speed, we introduce a pair of adaptive and lightweight group correlation layers to reduce sepia plex the impact of erroneous rectification and significantly improve model inference speed.

The experimental results show that the proposed approach achieves a competitive disparity estimation accuracy with a faster model inference speed than the current state-of-the-art methods.Notably, the model inference speed of the proposed approach is improved by 46.0% and 50.

4% in the SceneFlow test set and Middlebury benchmark, respectively.