Unsupervised Learning of Depth and Ego-Motion From Cylindrical Panoramic Video
Unsupervised Learning of Depth and Ego-Motion From Cylindrical Panoramic Video
复制标题
DOI：
10.1109/aivr46125.2019.00018
复制
发表时间：
2019-01
期刊：
影响因子：
--
通讯作者：
Alisha Sharma;Jonathan Ventura
中科院分区：
文献类型：
--
作者：
Alisha Sharma;Jonathan Ventura
我们介绍了一种卷积神经网络模型，用于从圆柱全景视频中进行深度和自我运动的无监督学习。全景深度估计是虚拟现实、三维建模和自主机器人导航等应用的重要技术。与以前将卷积神经网络应用于全景图像的方法相比，我们使用圆柱全景投影，允许使用传统的CNN层，如卷积滤波器和最大池化，而无需修改。我们对合成数据和真实的数据的评估表明，对圆柱全景图像的深度和自我运动的无监督学习可以产生高质量的深度图，并且增加的视场可以提高自我运动估计的准确性。我们还介绍了Headcam，这是一种新颖的全景视频数据集，它是在城市环境中骑自行车时从头盔式摄像机收集的。
We introduce a convolutional neural network model for unsupervised learning of depth and ego-motion from cylindrical panoramic video. Panoramic depth estimation is an important technology for applications such as virtual reality, 3d modeling, and autonomous robotic navigation. In contrast to previous approaches for applying convolutional neural networks to panoramic imagery, we use the cylindrical panoramic projection which allows for the use of the traditional CNN layers such as convolutional filters and max pooling without modification. Our evaluation of synthetic and real data shows that unsupervised learning of depth and ego-motion on cylindrical panoramic images can produce high-quality depth maps and that an increased field-of-view improves ego-motion estimation accuracy. We also introduce Headcam, a novel dataset of panoramic video collected from a helmet-mounted camera while biking in an urban setting.