论文标题
无监督的深度和自我动作从圆柱全景视频中使用，并应用了虚拟现实
Unsupervised Learning of Depth and Ego-Motion from Cylindrical Panoramic Video with Applications for Virtual Reality
论文作者
论文摘要
我们介绍了一个卷积神经网络模型，用于从圆柱全景视频中无监督的深度学习和自我运动。全景深度估计是用于虚拟现实，3D建模和自动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反，我们使用了圆柱形全景投影，该投影允许使用传统的CNN层，例如卷积过滤器和最大池而无需修改。我们对合成和真实数据的评估表明，对圆柱形全景图像的深度和自我运动的学习无法学习可以产生高质量的深度图，并且观看率的增加可以提高自我运动的估计精度。我们创建了两个新数据集来评估我们的方法：使用Carla Simulator创建的合成数据集和Headcam，这是一种新颖的数据集，这是一种从头盔上的相机收集的全景视频数据集，同时在城市环境中骑自行车。我们还将网络应用于将单眼全景图转换为立体视图的问题。
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 create two new datasets to evaluate our approach: a synthetic dataset created using the CARLA simulator, and Headcam, a novel dataset of panoramic video collected from a helmet-mounted camera while biking in an urban setting. We also apply our network to the problem of converting monocular panoramas to stereo panoramas.
