Christian Schütz

Multi-feature matching algorithm for free-form 3D surface registration

Applications such as object digitizing, object recognition and object inspection need efficient surface matching algorithms. Several variants of an iterative closest point (ICP) matching algorithm have been proposed for such tasks. This paper proposes and analyzes a multi-feature ICP matching algorithm that includes the surface color and the surface orientation information. The matching error minimization keeps the original closed-form solution. Therefore, the convergence of the multi-feature ICP algorithm cannot be proven any more. However, experiments show successful convergence. Further experimental results applying the multi-feature ICP to free-form objects show a significant increase of the range of successful convergence range.

Geometric matching of 3D objects: assessing the range of successful initial configurations

This paper considers the matching of 3D objects by a geometric approach based on the iterative closest point algorithm (ICP), which, starting from an initial configuration of two rigid objects, iteratively finds their best correspondence. The algorithm does not converge always to the best solution. It can be trapped in a local minimum and miss the optimum matching. While the convergence of this algorithm towards the global minimum is known to depend largely on the initial configuration of test and model objects, this paper investigates the quantitative nature of this dependence. Considering the space C of relative configurations of the two objects to be compared, we call range of successful initial configurations, or SIC-range, the subspace of C which configurations bring the algorithm to converge to the global minimum. In this paper, we present a frame for analyzing the SIC-range of 3D objects and present a number of original experimental results assessing the SIC-range of a number of real 3D objects.

Semi-automatic 3D Object Digitizing System Using Range Images

Manual object digitizing is a tedious task and can be replaced by 3D scanners which provide an accurate and fast way to digitize solid objects. Since only one view of an object can be captured at once, several views have to be combined in order to obtain a description of the complete surface. In this paper a digitizing system is proposed which captures and triangulates views of a real world 3D object and semi-automatically registers and integrates them into a virtual model. This process is divided into three steps. First, an object is placed at different poses and its surfaces are sensed by a range scanner. Then, the different surfaces are aligned automatically starting from a pose estimate entered interactively. Finally, the overlapping triangle meshes of the registered surfaces are fused in order to obtain one unique mesh for the entire object.

Toward the recognition of 3D free-form objects

This paper investigates a new approach for the recognition of 3D objects of arbitrary shape. The proposed solution follows the principle of model-based recognition using geometric 3D models and geometric matching. It is an alternative to the classical segmentation and primitive extraction approach and provides a perspective to escape some of its difficulties to deal with free-form shapes. The heart of this new approach is a recently published iterative closest point matching algorithm, which is applied variously to a number of initial configurations. We examine methods to obtain successful matching. Our investigations refer to a recognition system used for the pose estimation of 3D industrial objects in automatic assembly, with objects obtained from range data. The recognition algorithm works directly on the 3D coordinates of the objects surface as measured by a range finder. This makes our system independent of assumptions on the objects geometry. Test and model objects are sets of 3D points to be compared with the iterative closest point matching algorithm. Substantially, we propose a set of rules to choose promising initial configurations for the iterative closest point matching; an appropriate quality measure which permits reliable decision; a method to represent the object surface in a way that improves computing time and matching quality. Examples demonstrate the feasibility of this approach to free-form recognition.

Augmented reality using range images

This paper proposes range imaging as a means to improve object registration in an augmented reality environment. The addressed problem deals with virtual world construction from complex scenes using object models. During reconstruction, the scene view is augmented by superimposing virtual object representations from a model database. The main difficulty consists in the precise registration of a virtual object and its counterpart in the real scene. The presented approach solves this problem by matching geometric shapes obtained from range imaging. This geometric matching snaps the roughly placed object model onto its real world counterpart and permits the user to update the virtual world with the recognized model. We present a virtual world construction system currently under development that allows the registration of objects present in a scene by combined use of user interaction and automatic geometric matching based on range images. Potential applications are teleoperation of complex assembly tasks and world construction for mobile robotics.

Change detection in range imaging for 3D scene segmentation

This paper deals with the problem of segmenting a 3D scene obtained by range imaging. It assumes scenes of arbitrary complexity in which the objects to be recognized are newly added or removed and investigates how the methods of change detection and image difference used in classical image processing can be used in range imaging. In a first step, we consider the case of ideal range images and conduct an analysis of segmentation by range image difference that shows the direct applicability of this principle. In a second step, we consider the case of the wide class of range sensors that suffer from shadowing effects which leads to missing data in the range image. An interpretation of this ambiguity in difference calculation and means to remove it will be given. Additional rules for the practical segmentation of 3D scenes by range image change detection are described. The presented methods lead to the possibility to segment a scene by isolating newly added or removed objects. They are tested using range images from two distinct range imagers of the light stripping type. Results indicate the success of this approach and the practical possibility to use it in the frame of an assembly task.

Color digitizing and modeling of free-form 3D objects

This paper deals with the problem of capturing the color information of physical 3D objects thanks to a class of digitizers providing color and range data, like range finders based on structured lighting. It appears typically in a modeling procedure that aims at building a realistic virtual 3D model. The color data delivered by such scanners basically express the reflected color intensity of the object and not its intrinsic color. A consequence is therefore the existence, on the reconstructed model, of strong color discontinuities, which results from acquisition done under different illumination conditions. The paper considers three approaches in order to remove these discontinuities and obtained the desired intrinsic color data. The first one converts the reflected color intensity into the intrinsic color by computation, using a reflectance model and known acquisition parameters. The use of simple reflectance models is considered: Lambert and Phong, respectively for perfectly diffuse and mixed diffuse and specular reflection. The second approach is a hardware solution. It aims at using a nearly constant, diffuse and omnidirectional illumination over the visible parts of the object. A third method combines the first computational approach with the use of several known illumination sources. An experimental comparison of these three approaches is finally presented.

Free-form 3D object reconstruction from range images

The increasing use of virtual object representations for several applications creates a need for fast and simple object digitizing systems. Range finders provide a convenient way to digitize solid objects and permit the accurate and fast scanning of an object shape without any probe contact. However, only one view of an object can be captured at once, and therefore, for most objects, several views have to be combined in order to obtain a description of the complete surface. We consider a digitizing system which captures and triangulates views of a real-world 3D object and finally registers and integrates them. An interactive 3D environment allows the operator to enter an estimate of the relative pose of the different views, which are then aligned automatically by geometric matching. A new fusion algorithm is proposed which takes advantage of the previous view matching to remove the redundant overlap area of two views and to fuse together their respective meshes by a gap-filling algorithm.

3D vision in a virtual reality robotics environment

Virtual reality robotics (VRR) needs sensing feedback from the real environment. To show how advanced 3D vision provides new perspectives to fulfill these needs, this paper presents an architecture and system that integrates hybrid 3D vision and VRR and reports about experiments and results. The first section discusses the advantages of virtual reality in robotics, the potential of a 3D vision system in VRR and the contribution of a knowledge database, robust control and the combination of intensity and range imaging to build such a system. Section two presents the different modules of a hybrid 3D vision architecture based on hypothesis generation and verification. Section three addresses the problem of the recognition of complex, free- form 3D objects and shows how and why the newer approaches based on geometric matching solve the problem. This free- form matching can be efficiently integrated in a VRR system as a hypothesis generation knowledge-based 3D vision system. In the fourth part, we introduce the hypothesis verification based on intensity images which checks object pose and texture. Finally, we show how this system has been implemented and operates in a practical VRR environment used for an assembly task.

Paerformance of free-form 3D object recognition from range images

This paper investigates the recognition performance of a geometric matching approach to the recognition of free-form objects obtained from range images. The heart of this approach is a closest point matching algorithm which, starting from an initial configuration of two rigid objects, iteratively finds their best correspondence. While the effective performance of this algorithm is known to depend largely on the chosen set of initial configurations, this paper investigates the quantitative nature of this dependence. In essence, we experimentally measure the range of successful configurations for a set of test objects and derive quantitative rules for the recognition strategy. These results show the conditions under which the closest point matching algorithm can be successfully applied to free-form 3D object recognition and help to design a reliable and cost-effective recognition system.