Recently, records on stereo matching benchmarks are constantly broken by end-to-end disparity networks. However, the domain adaptation ability of these deep models is quite limited. Addressing such problem, we present a novel domain-adaptive approach called AdaStereo that aims to align multi-level representations for deep stereo matching networks. Compared to previous methods, our AdaStereo realizes a more standard, complete and effective domain adaptation pipeline. Firstly, we propose a non-adversarial progressive color transfer algorithm for input image-level alignment. Secondly, we design an efficient parameter-free cost normalization layer for internal feature-level alignment. Lastly, a highly related auxiliary task, self-supervised occlusion-aware reconstruction is presented to narrow the gaps in output space. We perform intensive ablation studies and break-down comparisons to validate the effectiveness of each proposed module. With no extra inference overhead and only a slight increase in training complexity, our AdaStereo models achieve state-of-the-art cross-domain performance on multiple benchmarks, including KITTI, Middlebury, ETH3D and DrivingStereo, even outperforming some state-of-the-art disparity networks finetuned with target-domain ground-truths. Moreover, based on two additional evaluation metrics, the superiority of our domain-adaptive stereo matching pipeline is further uncovered from more perspectives. Finally, we demonstrate that our method is robust to various domain adaptation settings, and can be easily integrated into quick adaptation application scenarios and real-world deployments.
翻译:最近,立体匹配基准记录不断因端对端差异网络而破碎。然而,这些深层模型的域性适应能力非常有限。在解决这些问题时,我们展示了一种名为AdaStereo的新型域性适应性方法,其目的是将深度立体匹配网络的多层次代表结构相匹配。与以往的方法相比,我们的AdaStereo实现了更标准、完整和有效的域性适应管道。首先,我们提出了投入图像水平匹配的非敌对性渐进色转换算法。第二,我们为内部地貌水平调整设计了一个高效的无参数标准化成本层。最后,提出了高度相关的辅助性任务、自我监督的域域域内封闭度重建,以缩小产出空间的差距。我们开展了密集的平流研究和分级比较,以验证每个拟议模块的有效性。由于没有额外的间接成本,培训复杂性也只有略微增加。我们AdaStereo模型可以在多种基准上实现最稳健的跨区域化业绩,包括KITTI、Midbury、ECTD和DrivinSter-deal-ad