SimFIR: A Simple Framework for Fisheye Image Rectification with
  Self-supervised Representation Learning
Hao Feng Wendi Wang Jiajun Deng Wengang Zhou Li Li Houqiang Li
In fisheye images, rich distinct distortion patterns are regularly
distributed in the image plane. These distortion patterns are independent of
the visual content and provide informative cues for rectification. To make the
best of such rectification cues, we introduce SimFIR, a simple framework for
fisheye image rectification based on self-supervised representation learning.
Technically, we first split a fisheye image into multiple patches and extract
their representations with a Vision Transformer (ViT). To learn fine-grained
distortion representations, we then associate different image patches with
their specific distortion patterns based on the fisheye model, and further
subtly design an innovative unified distortion-aware pretext task for their
learning. The transfer performance on the downstream rectification task is
remarkably boosted, which verifies the effectiveness of the learned
representations. Extensive experiments are conducted, and the quantitative and
qualitative results demonstrate the superiority of our method over the
state-of-the-art algorithms as well as its strong generalization ability on
real-world fisheye images.