On Computing Three-Dimensional Camera Motion from Optical Flow Detected in Two Consecutive Frames

Norio Tagawa, Ming Yang

Abstract: This study deals with the problem of estimating camera motion from optical flow, which is the motion vector between consecutive frames. The problem is formulated as a geometric fitting problem using the values of the depth map as the nuisance parameters. It is a problem whose maximum likelihood estimation does not satisfy the Cramer–Rao lower bound, and it has long been known as the Neyman–Scott problem. One of the authors previously proposed an objective function for this problem that, when minimized, yields an estimator with less variance in the estimation error than that obtained by maximum likelihood estimation. The author also proposed linear and nonlinear optimization methods for minimizing the objective function. In this paper, we provide new knowledge on these methods and evaluate their effectiveness by examining methods with low estimation error and low computational cost in practice.