Using 3D Line Segments for Robust and Efficient Change Detection from Multiple Noisy Images

In this paper, we propose a new approach to change detection that is based on the appearance or disappearance of 3D lines, which may be short, as seen in a new image. These 3D lines are estimated automatically and quickly from a set of previously-taken learning-images from arbitrary view points and under arbitrary lighting conditions. 3D change detection traditionally involves unsupervised estimation of scene geometry and the associated BRDF at each observable voxel in the scene, and the comparison of a new image with its prediction. If a significant number of pixels differ in the two aligned images, a change in the 3D scene is assumed to have occurred. The importance of our approach is that by comparing images of lines rather than of gray levels, we avoid the computationally intensive, and some-times impossible, tasks of estimating 3D surfaces and their associated BRDFs in the model-building stage. We estimate 3D lines instead where the lines are due to 3D ridges or BRDF ridges which are computationally much less costly and are more reliably detected. Our method is widely applicable as man-made structures consisting of 3D line segments are the main focus of most applications. The contributions of this paper are: change detection based on appropriate interpretation of line appearance and disappearance in a new image; unsupervised estimation of “short” 3D lines from multiple images such that the required computation is manageable and the estimation accuracy is high.