This paper demonstrates how to reduce the hand labeling effort considerably by 3D information in an object detection task. In particular, we demonstrate how an efficient car detector for aerial images with minimal hand labeling effort can be build. We use an on-line boosting algorithm to incrementally improve the detection results. Initially, we train the classifier with a single positive (car) example, randomly drawn from a fixed number of given samples. When applying this detector to an image we obtain many false positive detections. We use information from a stereo matcher to detect some of these false positives (e.g. detected cars on a facade) and feed back this information to the classifier as negative updates. This improves the detector considerably, thus reducing the number of false positives. We show that we obtain similar results to hand labeling by iteratively applying this strategy. The performance of our algorithm is demonstrated on digital aerial images of urban environments.

We report on a new system to augment a 3D block model of a real city obtained from aerial photogrammetry or aerial laser scanning with geo-referenced terrestrial data of the facades. The terrestrial images are acquired by a hand-held digital consumer camera. The relative orientation of the photographs is calculated automatically and fitted towards the 3D block model with minimized human input using vanishing points. The extraction of 3D primitives on the facades is based on line matching over multiple oriented images. The introduced city information system delivers a fully 3D geographic information data set and is called MetropoGIS.

Abstract This paper presents a method to automatically calculate texture maps for a given three‐dimensional object out of a sequence of images. It is used in our image‐based modeling approach after the registration of the images and the geometric modeling is done. We show that the presented method uses the information from all images by implicitly applying a weighting function. Using this approach the consideration of modeled occlusions as well as the detection and removal of non‐modeled occlusions is accomplished. The final resolution of the texture maps can be adjusted on a pixel/cm basis. Copyright © 2002 John Wiley & Sons, Ltd.