摘要翻译：
估计图像序列中像素之间的对应关系是包括视觉辅助导航（如视觉里程计(VO)、视觉惯性里程计(VIO)和视觉同步定位与映射(VSLAM)）和异常检测在内的许多任务的关键第一步。介绍了一种新的无监督深度神经网络结构，称为视觉惯性流（VIFlow）网络，并通过一个无监督多假设深度神经网络接收灰度图像和超视觉惯性测量，演示了图像对应和光流估计。VIFlow学习组合异构传感器流和从未知的、非参数化的噪声分布中采样，以在源图像和目标图像之间的像素级对应映射上产生几个（在本工作中为4或8个）可能的假设。我们定量地将VIFlow与几种领先的仅视觉密集对应和流方法进行了比较，并显示出与所有性能与最先进(SOA)密集对应匹配方法相似的方法相比，运行时间显著减少，效率显著提高。我们也给出了定性的结果，说明VIFlow是如何用于检测异常独立运动的。
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英文标题：
《Multi-Hypothesis Visual-Inertial Flow》
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作者：
E. Jared Shamwell, William D. Nothwang, Donald Perlis
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最新提交年份：
2018
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分类信息：

一级分类：Electrical Engineering and Systems Science        电气工程与系统科学
二级分类：Image and Video Processing        图像和视频处理
分类描述：Theory, algorithms, and architectures for the formation, capture, processing, communication, analysis, and display of images, video, and multidimensional signals in a wide variety of applications. Topics of interest include: mathematical, statistical, and perceptual image and video modeling and representation; linear and nonlinear filtering, de-blurring, enhancement, restoration, and reconstruction from degraded, low-resolution or tomographic data; lossless and lossy compression and coding; segmentation, alignment, and recognition; image rendering, visualization, and printing; computational imaging, including ultrasound, tomographic and magnetic resonance imaging; and image and video analysis, synthesis, storage, search and retrieval.
用于图像、视频和多维信号的形成、捕获、处理、通信、分析和显示的理论、算法和体系结构。感兴趣的主题包括：数学，统计，和感知图像和视频建模和表示；线性和非线性滤波、去模糊、增强、恢复和重建退化、低分辨率或层析数据；无损和有损压缩编码；分割、对齐和识别；图像渲染、可视化和打印；计算成像，包括超声、断层和磁共振成像；以及图像和视频的分析、合成、存储、搜索和检索。
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一级分类：Computer Science        计算机科学
二级分类：Robotics        机器人学
分类描述：Roughly includes material in ACM Subject Class I.2.9.
大致包括ACM科目I.2.9类的材料。
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英文摘要：
  Estimating the correspondences between pixels in sequences of images is a critical first step for a myriad of tasks including vision-aided navigation (e.g., visual odometry (VO), visual-inertial odometry (VIO), and visual simultaneous localization and mapping (VSLAM)) and anomaly detection. We introduce a new unsupervised deep neural network architecture called the Visual Inertial Flow (VIFlow) network and demonstrate image correspondence and optical flow estimation by an unsupervised multi-hypothesis deep neural network receiving grayscale imagery and extra-visual inertial measurements. VIFlow learns to combine heterogeneous sensor streams and sample from an unknown, un-parametrized noise distribution to generate several (4 or 8 in this work) probable hypotheses on the pixel-level correspondence mappings between a source image and a target image . We quantitatively benchmark VIFlow against several leading vision-only dense correspondence and flow methods and show a substantial decrease in runtime and increase in efficiency compared to all methods with similar performance to state-of-the-art (SOA) dense correspondence matching approaches. We also present qualitative results showing how VIFlow can be used for detecting anomalous independent motion.
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PDF链接：
https://arxiv.org/pdf/1803.05727