论文标题
Adastereo：一种简单有效的自适应立体声匹配方法
AdaStereo: A Simple and Efficient Approach for Adaptive Stereo Matching
论文作者
论文摘要
最近，立体声匹配基准的记录不断被端到端差异网络打破。但是，这些深层模型的域适应能力非常差。在解决此类问题的情况下，我们提出了一种新型的域名自适应管道，称为Adastereo，旨在使深度立体声匹配网络的多级表示。与以前的自适应立体声匹配方法相比，我们的Adastereo意识到了更标准，完整，有效的域自适应管道。首先，我们为输入图像级比对提出了一种非对抗性的渐进色转移算法。其次，我们为内部特征级比对设计有效的无参数成本归一化层。最后，提出了一项高度相关的辅助任务，自我监督的咬合意识重建，以缩小输出空间中的差距。我们的Adastereo模型在包括Kitti，Middlebury，Eth3D和Drivingstereo在内的多个立体基准上实现了最先进的跨域性能，甚至超过了具有目标域基地真实性的固定差异网络。
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 poor. Addressing such problem, we present a novel domain-adaptive pipeline called AdaStereo that aims to align multi-level representations for deep stereo matching networks. Compared to previous methods for adaptive stereo matching, 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 down the gaps in output space. Our AdaStereo models achieve state-of-the-art cross-domain performance on multiple stereo benchmarks, including KITTI, Middlebury, ETH3D, and DrivingStereo, even outperforming disparity networks finetuned with target-domain ground-truths.
