Klaus Denker

Online Triangulation of Laser-Scan Data

Hand-held laser scanners are used massively in industry for reverse engineering and quality measurements. In this process, it is difficult for the human operator to scan the target object completely and uniformly. Therefore, an interactive triangulation of the scanned points can assist the operator in this task.

On-Line Reconstruction of CAD Geometry

In reverse engineering and computer-aided design (CAD) applications point cloud data is usually manually scanned, reconstructed, and post-processed in separated steps. When point cloud data resulting from a scanning process do not satisfy certain necessary reconstruction requirements, one must perform scanning again to enable proper reconstruction. On-line reconstruction of 3d geometry allows one to generate and update a CAD reconstruction on-line during the scanning process with an hand-held laser scanner. Thus, regions where the scanned data is insufficient for the reconstruction are detected on the fly to allow an immediate correction and improvement of the scanned data. This enables the operator to focus on critical regions in the scanned data to improve the reconstruction quality. We present an on-line segmentation and on-line reconstruction of basic geometric primitives. The presented methods allow for a real-time processing of a point stream. They utilize data structures that can be updated at any time when additional data from the stream has to be processed. This data is used to complete and improve the segmentation and reconstruction during the scanning process.

Real-time triangulation of point streams

Hand-held laser scanners are commonly used in industry for reverse engineering and quality measurements. In this process, it is difficult for the human operator to scan the target object completely and uniformly. Therefore, an interactive triangulation of the scanned points can assist the operator in this task. In this paper, we describe the technical and implementational details of our real-time triangulation approach for point streams, presented at the 17th International Meshing Roundtable. Our method computes a triangulation of the point stream generated by the laser scanner online, i.e., the data points are added to the triangulation as they are received from the scanner. Multiple scanned areas and areas with a higher point density result in a finer mesh and a higher accuracy. On the other hand, the vertex density adapts to the estimated surface curvature. To guide the operator, the resulting triangulation is rendered with a visualization of its uncertainty and the display of an optimal scanning direction.

Hybrid face recognition based on real-time multi-camera stereo-matching

Multi-camera systems and GPU-based stereo-matching methods allow for a real-time 3d reconstruction of faces. We use the data generated by such a 3d reconstruction for a hybrid face recognition system based on color, accuracy, and depth information. This system is structured in two subsequent phases: geometry-based data preparation and face recognition using wavelets and the AdaBoost algorithm. It requires only one reference image per person. On a data base of 500 recordings, our system achieved detection rates ranging from 95% to 97% with a false detection rate of 2% to 3%. The computation of the whole process takes around 1.1 seconds.

Support Vector Machines for Classification of Geometric Primitives in Point Clouds

Classification of point clouds by different types of geometric primitives is an essential part in the reconstruction process of CAD geometry. We use support vector machines (SVM) to label patches in point clouds with the class labels tori, ellipsoids, spheres, cones, cylinders or planes. For the classification features based on different geometric properties like point normals, angles, and principal curvatures are used. These geometric features are estimated in the local neighborhood of a point of the point cloud. Computing these geometric features for a random subset of the point cloud yields a feature distribution. Different features are combined for achieving best classification results. To minimize the time consuming training phase of SVMs, the geometric features are first evaluated using linear discriminant analysis (LDA).

A {Hand}-held {Laser} {Scanner} based on {Multi}-camera {Stereo}-matching

Most laser scanners in engineering are extended versions of tactile measuring machines. These high precision devices are typically very expensive and hardware modifications are not possible without impairing the precision of the device. For these reasons we built our own laser-scanner system. It is based on a multi-camera reconstruction system developed for fast 3D face reconstructions. Based on this camera system, we developed a laser-scanner using GPU accelerated stereo-matching techniques and a hand-held line-laser probe. The resulting reconstruction is solely based on the known camera positions and parameters. Thus, it is not necessary to track the position and movement of the line-laser probe. This yields an inexpensive laser-scanner system where every hardware component can be modified individually for experiments and future extensions of the system.

On-line CAD Reconstruction with Accumulated Means of Local Geometric Properties

Reconstruction of hand-held laser scanner data is used in industry primarily for reverse engineering. Traditionally, scanning and reconstruction are separate steps. The operator of the laser scanner has no feedback from the reconstruction results. On-line reconstruction of the CAD geometry allows for such an immediate feedback.

Learning Geometric Primitives in Point Clouds

Primitive recognition in 3D point clouds is an important aspect in reverse engineering. We propose a method for primitive recognition based on machine learning approaches. The machine learning approaches used for the classification are linear discriminant analysis (LDA) and multi-class support vector machines (SVM). For the classification process local geometric properties (features) of the point cloud are computed based on point relations, normals, and principal curvatures. For the training phase point clouds are generated using a simulation of a laser scanning device based on ray tracing with an error model. The classification rates of novel, curvaturebased geometric features are compared to known geometric features to prove the effectiveness of the approach.

Survey on Benchmarks for a GPU Based Multi Camera Stereo Matching Algorithm

Stereo matching algorithms and multi camera reconstruction algorithms are usually compared using benchmarks. These benchmarks compare the quality of the resulting depth map or reconstructed surface mesh. We describe the differences between several known stereo and multi-view stereo benchmarks and their various datasets. Also the modifications that are necessary to use our own GPU based multi camera stereo matching algorithm with the data from these benchmarks are discussed.