Abstract not found

Non-rigid 3D shape correspondence is a fundamental and difficult problem. Most applications which require a correspondence rely on manually selected markers. Without user assistance, the performances of existing automatic correspondence methods depend strongly on a good initial shape alignment or shape prior, and they generally do not tolerate large shape variations. We present an automatic feature correspondence algorithm capable of handling large, non-rigid shape variations, as well as partial matching. This is made possible by leveraging the power of state-of-the-art mesh deformation techniques and relying on a combinatorial tree traversal for correspondence search. The search is deformation-driven, prioritized by a self-distortion energy measured on meshes deformed according to a given correspondence. We demonstrate the ability of our approach to naturally match shapes which differ in pose, local scale, part decomposition, and geometric detail through numerous examples.

We describe a method for 3D object scanning by aligning depth scans that were taken from around an object with a time-of-flight camera. These ToF cameras can measure depth scans at video rate. Due to comparably simple technology they bear potential for low cost production in big volumes. Our easy-to-use, cost-effective scanning solution based on such a sensor could make 3D scanning technology more accessible to everyday users. The algorithmic challenge we face is that the sensor’s level of random noise is substantial and there is a non-trivial systematic bias. In this paper we show the surprising result that 3D scans of reasonable quality can also be obtained with a sensor of such low data quality. Established filtering and scan alignment techniques from the literature fail to achieve this goal. In contrast, our algorithm is based on a new combination of a 3D superresolution method with a probabilistic scan alignment approach that explicitly takes into account the sensor’s noise characteristics.

In this paper, we describe a new algorithm for detecting structural redundancy in geometric data sets. Our algorithm computes rigid symmetries, i.e., subsets of a surface model that reoccur several times within the model differing only by translation, rotation or mirroring. Our algorithm is based on matching locally coherent constellations of feature lines on the object surfaces. In comparison to previous work, the new algorithm is able to detect a large number of symmetric parts without restrictions to regular patterns or nested hierarchies. In addition, working on relevant features only leads to a strong reduction in memory and processing costs such that very large data sets can be handled. We apply the algorithm to a number of real world 3D scanner data sets, demonstrating high recognition rates for general patterns of symmetry.

In this paper, we address the problem of detecting partial symmetries in 3D objects. In contrast to previous work, our algorithm is able to match deformed symmetric parts: We first develop an algorithm for the case of approximately isometric deformations, based on matching graphs of surface feature lines that are annotated with intrinsic geometric properties. The sensitivity to non-isometry is controlled by tolerance parameters for each such annotation. Using large tolerance values for some of these annotations and a robust matching of the graph topology yields a more general symmetry detection algorithm that can detect similarities in structures that have undergone strong deformations. This approach for the first time allows for detecting partial intrinsic as well as more general, non-isometric symmetries. We evaluate the recognition performance of our technique for a number

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Cultural objects of archeological findings are often broken and fractured into a large amount of fragments, and the archeologists are confronted by 3D puzzles when reassembling the fractured objects. Scanning the fragments and reassembling the corresponding 3D objects virtually is an elegant (and sometimes the only) solution. An efficient user interaction for the complex task to orientate or position two 3D objects relative to each other is essential, eventually in addition to automatic matching techniques. In this paper, we present ArcheoTUI, a new tangible user interface for the efficient assembly of the 3D scanned fragments of fractured archeological objects. The key idea is to use tangible props for the manipulation of the virtual fragments. In each hand, the user manipulates an electromagnetically tracked prop, and the translations and rotations are directly mapped to the corresponding virtual fragments on the display. For each hand, a corresponding foot pedal is used to clutch the movements of the hands. Hence, the hands of the user can be repositioned, or the user can be switched. The software of ArcheoTUI is designed to easily change assembly hypotheses, beyond classical undo/redo, by using a scene graph. We designed ArcheoTUI on the demand of archeaologists and in a direct collaboration with them, and we conducted a user study on site at their workplace. This user study revealed that the interface, and especially the foot pedal, was accepted, and that all the users managed to solve simple assembly tasks. In a case study, we show the assembly of one of their fractured archeological findings.

One of the main problems faced during reconstruction of fractured archaeological artifacts is sorting through a large number of candidate matches between fragments to find the relatively few that are correct. Previous computer methods for this task provided scoring functions based on a variety of properties of potential matches, including color and geometric compatibility across fracture surfaces. However, they usually consider only one or at most a few properties at once, and therefore provide match predictions with very low precision. In this paper, we investigate a machine learning approach that computes the probability that a match is correct based on the combination of many features. We explore this machine learning approach for ranking matches in three different sets of fresco fragments, finding that classifiers based on many match properties can be significantly more effective at ranking proposed matches than scores based on any single property alone. Our results suggest that it is possible to train a classifier on match properties in one data set and then use it to rank predicted matches in another data set effectively. We believe that this approach could be helpful in a variety of cultural heritage reconstruction systems.

In this report, we present a novel feature detection technique for unstructured point clouds. We introduce a generalized concept of geometric features that detects locally uniquely identifiable keypoints as centroids of area with locally minimal slippage. We extend the concept to multiple scales and extract features using multi-scale mean shift clustering. In order to validate matches between feature points, we employ a two stage technique that first sorts out unlikely matches, followed by an approximate alignment between remaining features by a rotational crosscorrelation analysis and a local iterative closest point (ICP) registration. The resulting residuals are then used as final similarity measure. The proposed combination of techniques results in a robust and reliable correspondence detection technique that yields registration results in situations where previous techniques are not able to detect usable feature correspondences. We provide a detailed empirical analysis of the method, and apply the technique to global registration, symmetry detection and deformable matching problems.

In recent years, there has been enormous growth in the number of 3D models and their availability to a wide segment of the population. Examples include the National Design Repository which stores 3D computer-aided design (CAD) models for tens of thousands of mechanical parts, the Protein Data Bank (PDB) that has atomic positions for tens of thousands of protein molecules, and the Princeton Shape Benchmark with thousands of everyday objects represented as polygonal surface models. With the availability of free interactive tools for creating 3D models and graphics cards for home computers, we can expect 3D data to become ever more widely available. Given the availability of 3D data, searching for a 3D object in a large database is a core problem for numerous applications including object recognition and the reuse of expertly created data. This raises two key research problems: 1) How can we improve search techniques? and 2) How do we evaluate 3D search techniques? The first contribution of this dissertation is an analysis technique to select the most important or distinctive regions of an object. Our approach identifies regions of a surface