We propose a novel approach to detect vanishing points in images using a convolutional neural network (CNN) trained on a newly collected Google street-view image dataset. By utilizing the camera parameters and road direction data from Google street view, we collected a total of 1,053,425 images with inferred ground-truth vanishing points, along 23 world-wide routes totaling 125,165 kilometers. We then formulate vanishing point detection as a CNN classification problem using an output layer with 225 discrete possible vanishing point locations. Experimental results show that our deep vanishing point system outperforms the state-of-the-art algorithmic vanishing point detector. We achieved 99% accuracy in recovering the horizon line and 92% in locating the vanishing point within a ±5-degree range.
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机译：我们提出了一种使用在新收集的Google街道视图图像数据集上培训的卷积神经网络（CNN）来检测图像中的图像中消失点的新方法。通过利用Google Street View的摄像机参数和道路方向数据，我们共收集了1,053,425个图像，其中包括推断的地面实例消失点，沿着23个全球航线总额125,165公里。然后，我们使用具有225个离散可能的消失点位置的输出层将消失点检测作为CNN分类问题。实验结果表明，我们的深层消失点系统优于最先进的算法消失点检测器。我们在恢复地平线中获得了99％的准确性，并将消失点定位在±5度范围内的92％。
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