Deriving Orientation Cues from Stereo Images

Abstract

In this paper, we investigate algorithms for evaluating surface orientation from pairs of stereo images using limited calibration information, and without reconstructing an explicit metric representation of the observed scene. We describe and compare two approaches based on the property that a given orientation at a point in space defines a homography between the two images. The first approach uses a parameterization of image deformation, and only requires knowledge of the epipolar geometry. The other one uses a more robust 3D parameterization, but requires approximate knowledge of some other calibration parameters. Finally, we introduce a probabilistic model that accounts for the degradation of the slope estimates as the points get further away from the cameras, and allows to compute the probability that the surface orientation at any given point is within some angular tolerance from the orientation of a reference plane. We show results on real images and investigate a potential application to robot motion planning.