Abstract
The focal length information of an image is indispensable for many computer vision tasks. In general, focal length can be obtained via camera calibration using specific planner patterns. However, for images taken by an unknown device, focal length can only be estimated based on the image itself. Currently, most of the single-image focal length estimation methods make use of predefined geometric cues (such as vanishing points or parallel lines) to infer focal length, which constrains their applications mainly on manmade scenes. The machine learning algorithms have demonstrated great performance in many computer vision tasks, but these methods are seldom used in the focal length estimation task, partially due to the shortage of labeled images for training the model. To bridge this gap, we first introduce a large-scale dataset FocaLens, which is especially designed for single-image focal length estimation. Taking advantage of the FocaLens dataset, we also propose a new focal length estimation model, which exploits the multiscale detection architecture to encode object distributions in images to assist focal length estimation. Additionally, an online focal transformation approach is proposed to further promote the model’s generalization ability. Experimental results demonstrate that the proposed model trained on FocaLens can not only achieve state-of-the-art results on the scenes with distinct geometric cues but also obtain comparable results on the scenes even without distinct geometric cues.
