Search

3D building models, being the main part of a digital city scene, are essential to all applications related to human activities in urban environments. The development of range sensors and Multi-View Stereo (MVS) technology facilitates our ability to automatically reconstruct level of details 2 (LoD2) models of buildings. However, because of the high complexity of building structures, no fully automatic system is currently available for producing building models. In order to simplify the problem, a lot of research focuses only on particular buildings shapes, and relatively simple ones. In this paper, we analyze the property of topology graphs of object surfaces, and find that roof topology graphs have three basic elements: loose nodes, loose edges, and minimum cycles. These elements have interesting physical meanings: a loose node is a building with one roof face; a loose edge is a ridge line between two roof faces whose end points are not defined by a third roof face; and a minimum cycle represents a roof corner of a building. Building primitives, which introduce building shape knowledge, are defined according to these three basic elements. Then all buildings can be represented by combining such building primitives. The building parts are searched according to the predefined building primitives, reconstructed independently, and grouped into a complete building model in a CSG-style. The shape knowledge is inferred via the building primitives and used as constraints to improve the building models, in which all roof parameters are simultaneously adjusted. Experiments show the flexibility of building primitives in both lidar point cloud and stereo point cloud.

We present a multi-view stereo method that avoids producing hallucinated surfaces which do not correspond to real surfaces. Our approach to 3D reconstruction is based on the minimal s-t cut of the graph derived from the Delaunay tetrahedralization of a dense 3D point cloud, which produces water-tight meshes. This is often a desirable property but it hallucinates surfaces in complicated scenes with multiple objects and free open space. For example, a sequence of images obtained from a moving vehicle often produces meshes where the sky is hallucinated because there are no images looking from the above to the ground plane. We present a method for detecting and removing such surfaces. The method is based on removing perturbation sensitive parts of the reconstruction using multiple reconstructions of perturbed input data. We demonstrate our method on several standard datasets often used to benchmark multi-view stereo and show that it outperforms the state-of-the-art techniques .

We propose a novel method for the multi-view reconstruction problem. Surfaces which do not have direct support in the input 3D point cloud and hence need not be photo-consistent but represent real parts of the scene (e.g. low-textured walls, windows, cars) are important for achieving complete reconstructions. We augmented the existing Labatut CGF 2009 method with the ability to cope with these difficult surfaces just by changing the t-edge weights in the construction of surfaces by a minimal s-t cut. Our method uses Visual-Hull to reconstruct the difficult surfaces which are not sampled densely enough by the input 3D point cloud. We demonstrate importance of these surfaces on several real-world data sets. We compare our improvement to our implementation of the Labatut CGF 2009 method and show that our method can considerably better reconstruct difficult surfaces while preserving thin structures and details in the same quality and computational time.