A method for the registration of attributed range images (original) (raw)

Range image registration driven by a hierarchy of surfaces

One of the 3-D vision tasks is to create a full 3-D model of an object. Only the partial range images from different view points can be obtained by real range finders. it is possible to get partially overlapped rang images. The views differ by Euclidean motion. This motions have to be known for combining the views. This article describes a method for registration of range images. Our algorithm is based on matching of differential structures of the surface. Results of this algorithm are used as first estimate in the distance minimization algorithm-- ...

Image-Based Registration of 3D-Range Data Using Feature Surface Elements

Digitizing real-life objects via range scanners, stereo vision or tactile sensors usually requires the composition of multiple range images. In this paper we exploit intensity images often recorded with the range data and propose a fully automatic registration technique using 2D-image features with intrinsic scale information for finding corresponding points on the 3D-views. In our approach, the fine registration of two range images is performed by first aligning the feature points themselves, followed by a so-called constrained-domain alignment step. In the latter, rather than feature points, we consider feature surface elements that are derived using the scale information inherently established with the 2D-features. The global registration error is minimized using graph relaxation techniques to mediate the transformations required to align the multiple range images. We demonstrate the power and feasibility of our method by a case-study in the cultural heritage domain.

Range Image Registration Driven By A Hierarchy Of Surface Differential Features

This work proposes a way how to register overlapping range images automatically. We explore the fact that the Euclidean transformation is determined by three pairs of corresponding points only. The main idea of the proposed approach is to reduce the number of points by nding intrinsic (signi cant) ones rst. For that, di erential structures of the surface as curves of zero-mean curvature which are invariant to Euclidean transformation are used. The di erential structures on a surface provide us with a hierarchy of intrinsic features, i.e. in a top down manner: surface ! curves ! points. The rst estimate of the Euclidean transform is done using points then it is re ned on curves and nally improved on surfaces. The performance of the approach is satisfactory for complicated surfaces which have rich di erential structure.

Range image registration driven by hierarchy of surface differential features

Proc. 22nd Workshop of the Austrian Association for Pattern Recognition, 1998

Abstract: This work proposes a way how to register overlapping range images automatically. We explore the fact that the Euclidean transformation is determined by three pairs of corresponding points only. The main idea of the proposed approach is to reduce the number of points by nding intrinsic (signi cant) ones rst. For that, di erential structures of the surface as curves of zero-mean curvature which are invariant to Euclidean transformation are used. The di erential structures on a surface provide us with a hierarchy of intrinsic features, ie in ...

Towards Automatic Registration Of Range Maps

2003

Abstract The range map registration (or alignment) phase is the main bottleneck in the 3D scanning pipeline due to the amount of user intervention required. The standard approach to registration consists of an initial rough alignment followed by an automatic refining technique (ICP). Automatic registration is an active research area, since it is the missing component to fully automatise the scanning process.

A review of recent range image registration methods with accuracy evaluation

Image and Vision Computing, 2007

The three-dimensional reconstruction of real objects is an important topic in computer vision. Most of the acquisition systems are limited to reconstruct a partial view of the object obtaining in blind areas and occlusions, while in most applications a full reconstruction is required. Many authors have proposed techniques to fuse 3D surfaces by determining the motion between the different views. The first problem is related to obtaining a rough registration when such motion is not available. The second one is focused on obtaining a fine registration from an initial approximation. In this paper, a survey of the most common techniques is presented. Furthermore, a sample of the techniques has been programmed and experimental results are reported to determine the best method in the presence of noise and outliers, providing a useful guide for an interested reader including a Matlab toolbox available at the webpage of the authors.

Semi-Automatic Range to Range Registration: A Feature-Based Method

Fifth International Conference on 3-D Digital Imaging and Modeling (3DIM'05), 2005

Our goal is the production of highly accurate photorealistic descriptions of the 3D world with a minimum of human interaction and increased computational efficiency. Our input is a large number of unregistered 3D and 2D photographs of an urban site. The generated 3D representations, after automated registration, are useful for urban planning, historical preservation, or virtual reality (entertainment) applications. A major bottleneck in the process of 3D scene acquisition is the automated registration of a large number of geometrically complex 3D range scans in a common frame of reference. We have developed novel methods for the accurate and efficient registration of a large number of 3D range scans. The methods utilize range segmentation and feature extraction algorithms. We have also developed a contextsensitive user interface to overcome problems emerging from scene symmetry.

Registration of point clouds using range and intensity information

International Workshop on Recording, Modeling and Visualization of Cultural Heritage, Ascona, Switzerland, May 22-27, E. Baltsavias, A. Gruen, L. Van Gool, M. Pateraki (Eds.), Taylor & Francis/Balkema, Leiden, pp. 115-126., 2005

An algorithm for the least squares matching of overlapping 3D surfaces is pre- sented. It estimates the transformation parameters between two or more fully 3D surfaces, using the Generalized Gauss-Markoff model, minimizing the sum of squares of the Euclidean dis- tances between the surfaces. This formulation gives the opportunity of matching arbitrarily ori- ented 3D surfaces simultaneously, without using explicit tie points. Besides the mathematical model and execution aspects we give further extensions of the basic model: simultaneous matching of multi sub-surface patches, and matching of surface geometry and its attribute in- formation, e.g. reflectance, color, temperature, etc. under a combined estimation model. We give practical examples for the demonstration of the basic method and the extensions.

Optimal registration of object views using range data

IEEE Transactions on Pattern Analysis and Machine Intelligence, 1997

This paper deals with robust registration of object views in the presence of uncertainties and noise in depth data. Errors in registration of multiple views of a 3D object severely a ect view integration during automatic construction of object models. We derive a minimum variance estimator (MVE) for computing the view transformation parameters accurately from range data of two views of a 3D object. The results of our experiments show that view transformation estimates obtained using MVE are signi cantly more accurate than those computed with an unweighted error criterion for registration.

Semi-Automatic Image-Based Co-Registration of Range Imaging Data with Different Characteristics

ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, 2011

Currently, enhanced types of active range imaging devices are available for capturing dynamic scenes. By using intensity and range images, data derived from different or the same range imaging devices can be fused. In this paper, an automatic image-based coregistration methodology is presented which uses a RANSAC-based scheme for the Efficient Perspective-n-Point (EPnP) algorithm. For evaluating the methodology, two different types of range imaging devices have been investigated, namely Microsoft Kinect and PMD [vision] CamCube 2.0. The data sets captured with the test devices have been compared to a reference device with respect to the absolute and relative accuracy. As the presented methodology can cope with different configurations concerning measurement principle, point density and range accuracy, it shows a high potential for automated data fusion for range imaging devices.