Contents 1 Introduction 7 1.1 Thesis Overview . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 7 1.2 General Overview of the City Scanning Project . . . . . . . . . . . . . . . . 8 1.2.1 The Pipeline . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 9 1.2.2 Acquisition . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 9 1.2.3 Pose Refinement . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 10 1.2.4 Facade Extraction . . . . . . . . . . . . . . . . . . . . . . . . . . . . 10 1.2.5 Input data and Objectives . . . . . . . . . . . . . . . . . . . . . . . . 11 2 Texture Estimation 12 2.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 12 2.1.1 Motivation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 12 2.1.2 Related Work . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 13 2.1.3 Overview . . . . . . . . . . . . . . . . . . . . . . . . . . . .

We present an imaging framework to acquire 3D surface scans at ultra high-resolutions (exceeding 600 samples per mm 2). Our approach couples a standard structured-light setup and photometric stereo using a large-format ultrahigh-resolution camera. While previous approaches have employed similar hybrid imaging systems to fuse positional data with surface normals, what is unique to our approach is the significant asymmetry in the resolution between the low-resolution geometry and the ultra-high-resolution surface normals. To deal with these resolution differences, we propose a multi-resolution surface reconstruction scheme that propagates the low-resolution geometric constraints through the different frequency bands while gradually fusing in the high-resolution photometric stereo data. In addition, to deal with the ultra-high-resolution images, our surface reconstruction is performed in a patch-wise fashion and additional boundary constraints are used to ensure patch coherence. Based on this multi-resolution reconstruction scheme, our imaging framework can produce 3D scans that show exceptionally detailed 3D surfaces far exceeding existing technologies. 1.

We present an application of our framework for 3-D object-centered change detection 1–4 to combined satellite and aerial imagery. In this framework, geometry is compared to geometry, allowing us to compare image sets with different acquisition conditions and even different sensors. By working in this framework, we do not encounter the restrictions and short-comings of conventional image-based change detection, which requires that the images being compared have similar acquisition geometry, photometry, scene illumination, and so forth. The contributions of our framework are: (1) using a geometric basis for change detection, allowing image sets acquired under different conditions to be compared; (2) explicit modeling of image geometry to be able to numerically characterize significant and insignificant change. The contributions of this paper are: (1) the algorithms are embedded in an integrated cartographic modeling and image processing system, which can ingest and make use of a variety of government and commercial imagery and geospatial data products; (2) experimentation with a variety of imagery and scene content. Modifications to the algorithms specific to their use with satellite imagery are discussed and the results from several experiments with both aerial and satellite images urban domains are described and analyzed.

Airborne laser scanner technique provides a 3D perception of the terrestrial topography, including true ground and objects belonging either to vegetated areas or to human made features. The high intrinsic accuracy and regularity of airborne laser sensors makes highly conceivable the extraction of semantic information related to the recorded 3D-points. In this respect, a new algorithm has been developed in order to classify the initial cloud of points into ground/non ground earth points and generate accurate Digital Terrain Models (DTMs) on a regular grid. Our approach is based on a multiple pass classification process. An estimation of the ground is performed within overlapping neighborhood and laser points are classified with regard to this ground estimation. The algorithm moves toward the neighbor where the average altitude is the lowest. We then compare the vicinity of the terrain with the estimated ground and apply a linear correction. As it goes along, points are filtered many times until we vote for the final label. The estimated ground surface is then the input of an energy minimization algorithm (ICM) which consider laser points as a set of attractors. The final DTM will be a trade off between internal properties and its closeness to ground laser points. The resolution may be fine enough to proceed relevant micro relief analysis especially in a rural environment. 1

In this paper we show that the reflectance function of a rotating object illuminated under a collinear light source (where the light source lies on or near the optical axis) can be estimated from the image sequence of the object and applied to surface recovery. We first calculate the 3D locations of some singular points from the image sequence, and extract the brightness values of these singular points during the object rotation to estimate the surface reflectance function. Then we use the estimated reflectance function for surface recovery from the images of the rotating object. Two subprocedures are used in surface recovery. The first subprocedure computes the depth around a point of known depth and surface orientation by using first-order Taylor series approximation. The other computes the surface orientation of a surface point from its image brightness values in the two different images by applying the estimated reflectance function. Starting from surface points of known depth valu...

We present a new approach to segmentation of 3Dimensional shapes that initializes and then optimizes a 3-D surface model given only the data and a very small number of 3-D seed points and corresponding surface normals. This is a valuable capability for medical, robotic and cartographic applications where such seed points can be naturally supplied. In effect, the surface model is clamped onto the object boundary in manner reminiscent of a Velcro being closed. We develop the method's mathematic framework and show preliminary results using volumetric medical data.

Related Work Other structures have been proposed to model visibility information in computer vision and computer graphics (e.g. aspect graphs [6] and visibility skeleton [2]), but they need a rigid 3D model as input and are not optimized for the same uses. In contrast to these, our strucinria-00590116,