论文标题
ADCPNET：自适应差异候选预测网络，用于有效的实时立体声匹配
ADCPNet: Adaptive Disparity Candidates Prediction Network for Efficient Real-Time Stereo Matching
论文作者
论文摘要
有效的实时差异估计对于在各个领域应用立体声视觉系统的应用至关重要。最近，基于粗到精细方法的立体网络在很大程度上减轻了大规模网络模型的内存限制和速度限制。然而，所有先前的粗到精细设计都采用恒定的偏移和三个或更多阶段来逐步完善粗略差异图，在部署在移动设备上时仍会导致不令人满意的计算精度和推理时间。本文声称，只要可以提供更准确的候选差异，就可以有效地纠正粗匹配误差。因此，我们提出了一个动态偏移预测模块，以满足不同对象的不同校正要求并设计有效的两阶段框架。此外，我们提出了与差异无关的卷积，以进一步提高性能，因为它与紧凑型成本量的局部统计特征更加一致。在多个数据集和平台上的评估结果清楚地表明，提议的网络的表现优于最先进的轻量级模型，尤其是对于移动设备而言，就精度和速度而言。代码将提供。
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. Besides, we propose a disparity-independent convolution to further improve the performance since it is more consistent with the local statistical characteristics of the compact cost volume. The evaluation results on multiple datasets and platforms clearly demonstrate that, the proposed network outperforms the state-of-the-art lightweight models especially for mobile devices in terms of accuracy and speed. Code will be made available.
