Published on Fri Apr 13 2018

FishEyeRecNet: A Multi-Context Collaborative Deep Network for Fisheye Image Rectification

Xiaoqing Yin, Xinchao Wang, Jun Yu, Maojun Zhang, Pascal Fua, Dacheng Tao

Images captured by fisheye lenses violate the pinhole camera assumption and suffer from distortions. Rectification is a crucial preprocessing step for many computer vision applications. In this paper, we propose an end-to-end multi-context collaborative deep network for removing distortions.

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Abstract

Images captured by fisheye lenses violate the pinhole camera assumption and suffer from distortions. Rectification of fisheye images is therefore a crucial preprocessing step for many computer vision applications. In this paper, we propose an end-to-end multi-context collaborative deep network for removing distortions from single fisheye images. In contrast to conventional approaches, which focus on extracting hand-crafted features from input images, our method learns high-level semantics and low-level appearance features simultaneously to estimate the distortion parameters. To facilitate training, we construct a synthesized dataset that covers various scenes and distortion parameter settings. Experiments on both synthesized and real-world datasets show that the proposed model significantly outperforms current state of the art methods. Our code and synthesized dataset will be made publicly available.

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