Full Camera Calibration from a Single View of Planar Scene full lers.This 1 Introduction camera visiomdetoh aeeie SkCcs5anaA2wFull Camera Calibration from a Single View of Planar Scene Yisong Chen1, Horace Ip2, Zhangjin Huang1, and Guoping Wang1 1 Key Laboratory of Machine Perception (Ministry of Education), Peking University 2 Department of Computer Science, City University of Hong Kong Abstract. We present a novel algorithm that applies conics to realize reliable camera calibration. In particular, we show that a single view of two coplanar circles is sufficiently powerful to give a fully automatic calibration framework that estimates both intrinsic and extrinsic parameters. This method stems from the previous work of conic based calibration and calibration-free scene analysis. It eliminates many a priori constraints such as known principal point, restric￾tive calibration patterns, or multiple views. Calibration is achieved statistically through identifying multiple orthogonal directions and optimizing a probability function by maximum likelihood estimate. Orthogonal vanishing points, which build the basic geometric primitives used in calibration, are identified based on the fact that they represent conjugate directions with respect to an arbitrary circle under perspective transformation. Experimental results from synthetic and real scenes demonstrate the effectiveness, accuracy, and popularity of the approach. 1 Introduction As an essential step for extracting metric 3D information from 2D images, camera cal￾ibration keeps an active research topic in most computer vision applications[9]. Much work has been devoted to camera calibration. They can be classified into two cate￾gories: (1D, 2D or 3D) Calibration pattern based algorithms, and multiple view based self-calibration approaches [15,19]. Conics and quadrics are widely accepted as most fundamental patterns in computer vision due to their elegant properties such as simple and compact algebraic expression, invariance under projective transformation, and robustness to image noise. Conics have long been employed to help perform camera calibration and pose estimation [6]. The strategy of using spheres as calibration pattern also draws more and more attention in recent years [1]. Vanishing point and vanishing line also play important roles in a lot of calibration and scene analysis work[12,14]. Under the assumption of zero skew and unit aspect ratio, all intrinsic parameters can be solved from the vanishing points of three mutually orthogonal directions in a single image[3]. Multiple patterns or views can be employed to perform calibration in the cases where not all three vanishing points are available from a single view. Although recent research has come up with fruitful achievements, most work suffers from the problems of multiple views, restricted patterns or incompleteness of solutions [5,13,18]. Two major obstacles are the mandatory requirements of multiple views and G. Bebis et al. (Eds.): ISVC 2008, Part I, LNCS 5358, pp. 815–824, 2008. c Springer-Verlag Berlin Heidelberg 2008