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arXiv · 1804.04784

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

Xiaoqing Yin·Xinchao Wang·Jun Yu·Maojun Zhang·Pascal Fua·Dacheng Tao
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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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Xiaoqing Yin, Xinchao Wang, Jun Yu, Maojun Zhang, Pascal Fua, Dacheng Tao. 2018-04-13. FishEyeRecNet: A Multi-Context Collaborative Deep Network for Fisheye Image Rectification. https://arxiv.org/abs/1804.04784

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