Airborne acquisition and on-road mobile mapping provide complementary 3D information of an urban landscape: the former acquires roof structures, ground, and vegetation at a large scale, but lacks the facade and street-side details, while the latter is incomplete for higher floors and often totally misses out on pedestrian-only areas or undriven districts. In this work, we introduce an approach that efficiently unifies a detailed street-side Structure-from-Motion (SfM) or Multi-View Stereo (MVS) point cloud and a coarser but more complete point cloud from airborne acquisition in a joint surface mesh. We propose a point cloud blending and a volumetric fusion based on ray casting across a 3D tetrahedralization (3DT), extended with data reduction techniques to handle large datasets. To the best of our knowledge, we are the first to adopt a 3DT approach for airborne street-side data fusion. Our pipeline exploits typical characteristics of airborne and ground data, and produces a seamless, watertight mesh that is both complete and detailed. Experiments on 3D urban data from multiple sources and different data densities show the effectiveness and benefits of our approach.
Last but not least, there exist only a few works that combine street-side and aerial data for joint mesh reconstruction @cite_3 @cite_23 @cite_9 . Fr "uh and Zakhor @cite_3 construct meshes over street-side LiDAR range maps and over a large-scale Digital Surface Model (DSM). Unlike in our approach, they reconstruct a facade and an airborne mesh separately without topological fusion. @cite_9 solves the problem by directly applying Poisson surface reconstruction @cite_7 over the joint dense point cloud computed by patch-based MVS @cite_13 without a cross-consistency check between airborne and street-side data. @cite_23 integrate over 200M points from a tripod-mounted ground LiDAR and an aerial DSM, by using a distance field over an octree and an out-of-core dual contouring approach. Despite the quite complex algorithm, results are noisy and contain many large holes. In contrast, our approach performs cross-consistency filtering and produces a good quality watertight surface.
@cite_7 @cite_9 @cite_3 @cite_23 @cite_13
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We show that surface reconstruction from oriented points can be cast as a spatial Poisson problem. This Poisson formulation considers all the points at once, without resorting to heuristic spatial partitioning or blending, and is therefore highly resilient to data noise. Unlike radial basis function schemes, our Poisson approach allows a hierarchy of locally supported basis functions, and therefore the solution reduces to a well conditioned sparse linear system. We describe a spatially adaptive multiscale algorithm whose time and space complexities are proportional to the size of the reconstructed model. Experimenting with publicly available scan data, we demonstrate reconstruction of surfaces with greater detail than previously achievable. We present the first large scale system for capturing and rendering relight able scene reconstructions from massive unstructured photo collections taken under different illumination conditions and viewpoints. We combine photos taken from many sources, Flickr-Based ground-level imagery, oblique aerial views, and street view, to recover models that are significantly more complete and detailed than previously demonstrated. We demonstrate the ability to match both the viewpoint and illumination of arbitrary input photos, enabling a Visual Turing Test in which photo and rendering are viewed side-by-side and the observer has to guess which is which. While we cannot yet fool human perception, the gap is closing. We describe an approach to register and merge detailed facade models with a complementary airborne model. The airborne modeling process provides a half-meter resolution model with a bird's-eye view of the entire area, containing terrain profile and building tops. The ground-based modeling process results in a detailed model of the building facades. Using the DSM obtained from airborne laser scans, we localize the acquisition vehicle and register the ground-based facades to the airborne model by means of Monte Carlo localization (MCL). We merge the two models with different resolutions to obtain a 3D model. This paper presents techniques for the merging of 3D data coming from different sensors, such as ground and aerial laser range scans. The 3D models created are reconstructed to give a photo-realistic scene enabling interactive virtual walkthroughs, measurements and scene change analysis. The reconstructed model is based on a weighted integration of all available data based on sensor-specific parameters such as noise level, accuracy, inclination and reflectivity of the target, spatial distribution of points. The geometry is robustly reconstructed with a volumetric approach. Once registered and weighed, all data is re-sampled in a multi-resolution distance field using out-of-core techniques. The final mesh is extracted by contouring the iso-surface with a feature preserving dual contouring algorithm. The paper shows results of the above technique applied to Verona (Italy) city centre. This paper proposes a novel algorithm for multiview stereopsis that outputs a dense set of small rectangular patches covering the surfaces visible in the images. Stereopsis is implemented as a match, expand, and filter procedure, starting from a sparse set of matched keypoints, and repeatedly expanding these before using visibility constraints to filter away false matches. The keys to the performance of the proposed algorithm are effective techniques for enforcing local photometric consistency and global visibility constraints. Simple but effective methods are also proposed to turn the resulting patch model into a mesh which can be further refined by an algorithm that enforces both photometric consistency and regularization constraints. The proposed approach automatically detects and discards outliers and obstacles and does not require any initialization in the form of a visual hull, a bounding box, or valid depth ranges. We have tested our algorithm on various data sets including objects with fine surface details, deep concavities, and thin structures, outdoor scenes observed from a restricted set of viewpoints, and "crowded" scenes where moving obstacles appear in front of a static structure of interest. A quantitative evaluation on the Middlebury benchmark [1] shows that the proposed method outperforms all others submitted so far for four out of the six data sets.