D Frère

Automatic Modeling and 3D Reconstruction of Urban House Roofs from High Resolution Aerial Imagery

Many tasks in modern urban planning require 3-dimensional (3D) spatial information, preferably in the form of 3D city models. Constructing such models requires automatic methods for reliable 3D building reconstruction. House roofs encountered in residential areas in European cities exhibit a wide variety in their shapes. This limits the use of predefined roof models for their reconstruction. The strategy put forward in this paper is, first, to construct a polyhedral model of the roof structure, which captures the topology of the roof, but which might not be very accurate in a metric sense; and then, in a second step, to improve the metric accuracy by fitting this model to the data. This decoupling of topology extraction from metric reconstruction allows a more efficient roof modelling involving less criteria. And, restricting the processing, at all stages, to one or just a few roof structures, by using a colour-based segmentation of the images, allows to use constraints that are not very tight. The approach has been tested on a state-of-the-art dataset of aerial images of residential areas in Brussels.

Automatic modelling and 3D reconstruction of urban buildings from aerial imagery

A method is presented that automatically generates 3D models of generic house roofs from aerial images of residential areas in urban sites. Crucial to the method is the possibility of delineating regions in the images that correspond well to actual roof structures. Restricting the processing to relatively small regions allows at all stages of the algorithm to use constraints that are not very tight, and, at the same time, to keep the combinatorics under control. All modelling is done by reasoning in 3D. By adopting a strategy of hypothesis generation and verification the authors are not only are capable of exploiting all available image data at every step in the algorithm, but also to treat all views equally. Decoupling topology retrieval from metric accuracy makes it possible to generate and test combinations which otherwise would have been ruled out by more tight constraints. The method is implemented and tests on the correctness and completeness of the extracted roof models have been performed.

On the reconstruction of urban house roofs from aerial images

A method is presented to automatically generate 3D models of house roofs from aerial images of residential areas in urban sites. Following the methodology of (Bignone et al. 1996) a house roof is modelled as a set of planar polygonal patches, each of which encloses a compact area with consistent photometric and chromatic properties, and that mutually adjoin along common boundaries. The approach presented here differs from that in (Bignone et al. 1996) in the sense that it starts by delineating in the images homogeneous regions which correspond to roof structures by navigating through a constraint triangulation network, and that line segment matching, 3D reconstruction, coplanar grouping and polygonal patch formation are initialized from corresponding regions in different views. Each polygon hypothesis is tested for consistency with the 2D and 3D data, and, if necessary, it is changed accordingly.

Semi-automatic modelling of urban buildings from high resolution aerial imagery

A method is presented to (semi-)automatically generate 3D models of urban buildings from aerial images. The process starts by selecting (with a mouse click) one or more roofs in the image(s). The program then automatically delineates the roof structures in the images and extracts straight line segments which are matched across different views and reconstructed in 3D. These 3D line segments are then grouped into coplanar sets and combined into polygonal shapes. Each polygon hypothesis is verified with respect to the 3D reconstruction and the original image data; and, if necessary, corrected accordingly. Subsequently, the polygons are combined into a polyhedral roof model. The emphasis here is on extracting the correct topology of the roof structure. Metric accuracy is obtained by back-projecting the model into the images and minimizing the total reprojection error. Finally, the building model is completed by adding vertical walls to the reconstructed roof. The approach is tested on a state-of-the-art dataset of aerial images of residential areas in Brussels.

3D reconstruction of generic house roofs from aerial images of urban areas

A method is presented to automatically generate 3D models of house roofs from aerial images of residential areas in urban sites. First, homogeneous regions with consistent photometric and chromatic triangulation network. Stereo matching of straight line segments is performed between corresponding regions only. Line segments that are matched across at lest three views are reconstructed by a bundle adjustment procedure. The reconstructed line segments are hypothesis is subjected to a consistency verification with respect to the 3D reconstruction and the original image data, and, if necessary, corrected accordingly. Observe that the combinatorics is kept under control by processing one region at the time. In a next stage, the polygons are glued together into a roof model. The emphasis here is on extracting the correct topology of the roof structure. Metric accuracy of the reconstruction is obtained in an additional step by backprojecting the recovered model of the roof structure onto the images and minimizing the total reprojection error. The viability of this approach has been tested on a state-of-the-art dataset or aerial images of residential areas in Brussels.

A 3-Dimensional Multi-View Based Strategy for Remotely Sensed Image Interpretation*

The aim of this paper is to assess the feasibility of extracting 3- dimensional information about man-made objects from very high resolution satellite imagery. To this end, a 3-dimensional, multi-view based paradigm is proposed. The underlying philosophy is to generate from the images reliable geometric 3D features, exploiting the multi-view geometric constraints for blunder correction. Scene analysis is performed by reasoning in 3D world space and verified in the images by means of a hypothesis generation and verification procedure in which the decisions are taken on the basis of a multi-view consensus. As an example of such an approach, a method for automatic modelling and 3D reconstruction of buildings is discussed and the effects of the resolution of the new generation satellite data on the performance of the algorithm and the metric accuracy of the final reconstruction is investigated.