Self-Calibration of Turntable Sequences from Silhouettes
This paper addresses the problem of recovering both the intrinsic and extrinsic parameters of a camera from the silhouettes of an object in a turntable sequence. Previous silhouette-based approaches have exploited correspondences induced by epipolar tangents to estimate the image invariants under turntable motion and achieved a weak calibration of the cameras. In order to recover the rotation angles and obtain a euclidean reconstruction, these approaches require the prior knowledge of the camera intrinsics. In this paper, we propose a novel approach for recovering the rotation angles precisely in the absence of the camera intrinsics. It is known that the fundamental matrix relating any two views in a turntable sequence can be expressed explicitly in terms of the image invariants, the rotation angle, and a fixed scalar. It will be shown that the imaged circular points for the turntable plane can also be formulated in terms of the same image invariants and fixed scalar. This allows the imaged circular points to be recovered directly from the estimated image invariants. The imaged circular points and the image invariants provide constraints for the estimation of the imaged absolute conic, and the camera calibration matrix can thus be recovered. A robust method for estimating the fixed scalar from image triplets is introduced, and a method for recovering the rotation angles by using the estimated imaged circular points and epipoles is presented. Using the estimated camera intrinsics and extrinsics, a euclidean reconstruction can be obtained. Experimental results on real data sequences are presented which demonstrate the high precision achieved by the proposed method.
self-calibration silhouettes turntable motion circular points