Abstract
Dense pixel matching is important for many computer vision tasks such as disparity and flow estimation. We present a robust, unified descriptor network that considers a large context region with high spatial variance. Our network has a very large receptive field and avoids striding layers to maintain spatial resolution. These properties are achieved by creating a novel neural network layer that consists of multiple, parallel, stacked dilated convolutions (SDC). Several of these layers are combined to form our SDC descriptor network. In our experiments, we show that our SDC features outperform state-of-the-art feature descriptors in terms of accuracy and robustness. In addition, we demonstrate the superior performance of SDC in state-of-the-art stereo matching, optical flow and scene flow algorithms on several famous public benchmarks.
Abstract (translated)
密集像素匹配对于许多计算机视觉任务，如视差和流量估计，都是非常重要的。我们提出了一个健壮的、统一的描述网络，它考虑了一个具有高空间方差的大上下文区域。我们的网络有一个非常大的接收场，并避免跨越层，以保持空间分辨率。这些特性是通过创建一个新的神经网络层来实现的，该层由多个、并行、堆叠的扩张卷积（SDC）组成。这些层中的几个被结合起来形成我们的SDC描述符网络。在我们的实验中，我们表明在准确性和鲁棒性方面，我们的SDC特性优于最先进的特性描述符。此外，我们还展示了SDC在最先进的立体匹配、光流和场景流算法方面在几个著名的公共基准点上的优越性能。
URL
https://arxiv.org/abs/1904.03076
