Camera Calibration with Lens Distortion from Low-rank Textures

We present a simple, accurate, and flexible method to calibrate intrinsic parameters of a camera together with (possibly significant) lens distortion. This new method can work under a wide range of practical scenarios: using multiple images of a known pattern, multiple images of an unknown pattern, single or multiple image(s) of multiple patterns etc. Moreover, this new method does not rely on extracting any low-level features such as corners or edges. It can tolerate considerably large lens distortion, noise, error, illumination and viewpoint change, and still obtain accurate estimation of the camera parameters. The new method leverages on the recent breakthroughs in powerful highdimensional convex optimization tools, especial those for matrix rank minimization and sparse signal recovery. We will show how the camera calibration problem can be formulated as an important extension to principal component pursuit, and solved by similar techniques. We characterize to exactly what extent...

• Type: Journal
• Year: 2011
• Where: CVPR
• Authors: Zhengdong Zhang, Yasuyuki Matsushita, Yi Ma
• Keywords: Camera, Computer Vision, CVPR 2011, Multiple Images, Parameters