  We propose a neural geometric parser learning single image camera calibration
for man-made scenes. Unlike previous neural approaches that rely only on
semantic cues obtained from neural networks, our approach considers both
semantic and geometric cues, resulting in significant accuracy improvement. The
proposed framework consists of two networks. Using line segments of an image as
geometric cues, the first network estimates the zenith vanishing point and
generates several candidates consisting of the camera rotation and focal
length. The second network evaluates each candidate based on the given image
and the geometric cues, where prior knowledge of man-made scenes is used for
the evaluation. With the supervision of datasets consisting of the horizontal
line and focal length of the images, our networks can be trained to estimate
the same camera parameters. Based on the Manhattan world assumption, we can
further estimate the camera rotation and focal length in a weakly supervised
manner. The experimental results reveal that the performance of our neural
approach is significantly higher than that of existing state-of-the-art camera
calibration techniques for single images of indoor and outdoor scenes.
