Adaptive Disparity Candidates Prediction Network for Efficient Real-Time Stereo Matching

Efficient real-Time disparity estimation is critical for the application of stereo vision systems in various areas. Recently, stereo network based on coarse-To-fine method has largely relieved the memory constraints and speed limitations of large-scale network models. Nevertheless, all of the previous coarse-To-fine designs employ constant offsets and three or more stages to progressively refine the coarse disparity map, still resulting in unsatisfactory computation accuracy and inference time when deployed on mobile devices. This paper claims that the coarse matching errors can be corrected efficiently with fewer stages as long as more accurate disparity candidates can be provided. Therefore, we propose a dynamic offset prediction module to meet different correction requirements of diverse objects and design an efficient two-stage framework. In addition, a disparity-independent convolution is proposed to regularize the compact cost volume efficiently and further improve the overall performance. The disparity quality and efficiency of various stereo networks are evaluated on multiple datasets and platforms. Evaluation results demonstrate that, the disparity error rate of the proposed network achieves 2.66% and 2.71% on KITTI 2012 and 2015 test sets respectively, where the computation speed is $2\times $ faster than the state-of-The-Art lightweight models on high-end and source-constrained GPUs.