2 research outputs found
Fisheye Distortion Rectification from Deep Straight Lines
This paper presents a novel line-aware rectification network (LaRecNet) to
address the problem of fisheye distortion rectification based on the classical
observation that straight lines in 3D space should be still straight in image
planes. Specifically, the proposed LaRecNet contains three sequential modules
to (1) learn the distorted straight lines from fisheye images; (2) estimate the
distortion parameters from the learned heatmaps and the image appearance; and
(3) rectify the input images via a proposed differentiable rectification layer.
To better train and evaluate the proposed model, we create a synthetic
line-rich fisheye (SLF) dataset that contains the distortion parameters and
well-annotated distorted straight lines of fisheye images. The proposed method
enables us to simultaneously calibrate the geometric distortion parameters and
rectify fisheye images. Extensive experiments demonstrate that our model
achieves state-of-the-art performance in terms of both geometric accuracy and
image quality on several evaluation metrics. In particular, the images
rectified by LaRecNet achieve an average reprojection error of 0.33 pixels on
the SLF dataset and produce the highest peak signal-to-noise ratio (PSNR) and
structure similarity index (SSIM) compared with the groundtruth
Wide-angle Image Rectification: A Survey
Wide field-of-view (FOV) cameras, which capture a larger scene area than
narrow FOV cameras, are used in many applications including 3D reconstruction,
autonomous driving, and video surveillance. However, wide-angle images contain
distortions that violate the assumptions underlying pinhole camera models,
resulting in object distortion, difficulties in estimating scene distance,
area, and direction, and preventing the use of off-the-shelf deep models
trained on undistorted images for downstream computer vision tasks. Image
rectification, which aims to correct these distortions, can solve these
problems. In this paper, we comprehensively survey progress in wide-angle image
rectification from transformation models to rectification methods.
Specifically, we first present a detailed description and discussion of the
camera models used in different approaches. Then, we summarize several
distortion models including radial distortion and projection distortion. Next,
we review both traditional geometry-based image rectification methods and deep
learning-based methods, where the former formulate distortion parameter
estimation as an optimization problem and the latter treat it as a regression
problem by leveraging the power of deep neural networks. We evaluate the
performance of state-of-the-art methods on public datasets and show that
although both kinds of methods can achieve good results, these methods only
work well for specific camera models and distortion types. We also provide a
strong baseline model and carry out an empirical study of different distortion
models on synthetic datasets and real-world wide-angle images. Finally, we
discuss several potential research directions that are expected to further
advance this area in the future.Comment: Accepted by the International Journal of Computer Vision (IJCV). Both
the datasets and source code are available at
https://github.com/loong8888/WAI