Due to the high similarity of disparity between consecutive frames in video sequences, the area where disparity changes is defined as the residual map, which can be calculated. Based on this, we propose RecSM, a network based on residual estimation with a flexible recursive structure for video stereo matching. The RecSM network accelerates stereo matching using a Multi-scale Residual Estimation Module (MREM), which employs the temporal context as a reference and rapidly calculates the disparity for the current frame by computing only the residual values between the current and previous frames. To further reduce the error of estimated disparities, we use the Disparity Optimization Module (DOM) and Temporal Attention Module (TAM) to enforce constraints between each module, and together with MREM, form a flexible Stackable Computation Structure (SCS), which allows for the design of different numbers of SCS based on practical scenarios. Experimental results demonstrate that with a stack count of 3, RecSM achieves a 4x speed improvement compared to ACVNet, running at 0.054 seconds based on one NVIDIA RTX 2080TI GPU, with an accuracy decrease of only 0.7%. Code is available at https://github.com/Y0uchenZ/RecSM.

翻译：由于视频序列中连续帧之间的视差具有高度相似性，视差发生变化的区域被定义为可计算的残差图。基于此，我们提出RecSM——一种基于残差估计、具有灵活递归结构的视频立体匹配网络。该网络通过多尺度残差估计模块（MREM）加速立体匹配过程，该模块以时序上下文为参考，仅计算当前帧与前一帧之间的残差值即可快速获得当前帧视差。为降低估计视差的误差，我们采用视差优化模块（DOM）与时序注意力模块（TAM）对各模块间施加约束，并与MREM共同构成灵活可堆叠的计算结构（SCS），可根据实际场景设计不同数量的SCS堆叠层。实验结果表明：在堆叠数量为3时，RecSM相较于ACVNet实现了4倍的速度提升，在单张NVIDIA RTX 2080TI GPU上运行时间为0.054秒，精度仅下降0.7%。代码公开于https://github.com/Y0uchenZ/RecSM。