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
this https URL

基于残差估计的递归结构网络 RecSM 用于视频立体匹配，通过计算当前帧与前一帧之间的残差值，仅利用时间上下文作为参考，快速计算当前帧的位移，同时使用 DOM 和 TAM 模块进一步减少估计误差，堆叠式计算结构 (SCS) 实现可扩展性。实验结果表明，RecSM 在一块 NVIDIA RTX 2080TI GPU 上运行 0.054 秒，相比 ACVNet 提高了 4 倍的速度，准确度仅下降 0.7%。