113 research outputs found
SimFIR: A Simple Framework for Fisheye Image Rectification with Self-supervised Representation Learning
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.Comment: Accepted to ICCV 202
FishRecGAN: An End to End GAN Based Network for Fisheye Rectification and Calibration
We propose an end-to-end deep learning approach to rectify fisheye images and
simultaneously calibrate camera intrinsic and distortion parameters. Our method
consists of two parts: a Quick Image Rectification Module developed with a
Pix2Pix GAN and Wasserstein GAN (W-Pix2PixGAN), and a Calibration Module with a
CNN architecture. Our Quick Rectification Network performs robust rectification
with good resolution, making it suitable for constant calibration in
camera-based surveillance equipment. To achieve high-quality calibration, we
use the straightened output from the Quick Rectification Module as a
guidance-like semantic feature map for the Calibration Module to learn the
geometric relationship between the straightened feature and the distorted
feature. We train and validate our method with a large synthesized dataset
labeled with well-simulated parameters applied to a perspective image dataset.
Our solution has achieved robust performance in high-resolution with a
significant PSNR value of 22.343.Comment: 18 pages, 7 figures, 4 tables, accepted by AAIML 202
- …