Abstract
Detecting the occlusion from stereo images or video frames is important to many computer vision applications. Previous efforts focus on bundling it with the computation of disparity or optical flow, leading to a chicken-and-egg problem. In this paper, we leverage convolutional neural network to liberate the occlusion detection task from the interleaved, traditional calculation framework. We propose a Symmetric Network (SymmNet) to directly exploit information from an image pair, without estimating disparity or motion in advance. The proposed network is structurally left-right symmetric to learn the binocular occlusion simultaneously, aimed at jointly improving both results. The comprehensive experiments show that our model achieves state-of-the-art results on detecting the stereo and motion occlusion.
Abstract (translated)
从立体图像或视频帧中检测遮挡对于许多计算机视觉应用来说是重要的。以前的努力集中在将其与差异或光流的计算捆绑在一起，导致鸡与蛋的问题。在本文中，我们利用卷积神经网络从交错的传统计算框架中解放遮挡检测任务。我们提出了一种对称网络（SymmNet）来直接利用来自图像对的信息，而无需提前估计差异或运动。所提出的网络在结构上是左右对称的，以同时学习双眼闭塞，旨在共同改善两种结果。综合实验表明，我们的模型在检测立体声和运动遮挡方面取得了最先进的成果。
URL
https://arxiv.org/abs/1807.00959
