Adaptive Window Stereo Matching Algorithm with Lightweight Transformer
The existing end-to-end stereo matching algorithms preset a fixed disparity range to reduce memory consumption and computation,making it difficult to balance matching accuracy and running efficiency.To solve this problem,this paper proposes an adaptive window stereo matching algorithm based on a lightweight Transformer.The coordinate attention layer with linear complexity is used to encode the position of the low-resolution feature map,which reduces the amount of calculation and enhances the discrimination of similar features.The lightweight Transformer feature description module is designed to convert context-related features,and a separable Multi-Head Self-Attention(MHSN)layer is introduced to reduce Transformer delay.The differentiable matching layer is used to match the features,and an adaptive window matching and refinement module is designed to perform sub-pixel matching and refinement,which improves matching accuracy and reduces video memory consumption,whereby after disparity regression,a disparity map can be generated regardless of the disparity range.The comparative experiments on KITTI2015,KITTI2012,and SceneFlow datasets showed that the proposed stereo matching algorithm is approximately 4.7 times faster than the standard Transformer-based STTR in matching efficiency and has friendlier storage performance.Compared with the PSMNet based on 3D convolution method,the mismatching rate was reduced by 18% and the running time was five times faster,achieving a better balance between speed and accuracy.
国家科技期刊平台
NSTL
万方数据
维普
