This paper presents a method for automatically registering multiple three dimensional (3D) data sets. Previous approaches required manual specification of initial pose estimates or relied on external pose measurement systems. In contrast, our method does not assume any knowledge of initial poses or even which data sets overlap. Our automatic registration algorithm begins by converting the input data into surface meshes, which are pair-wise registered using a surface matching engine. The resulting matches are tested for surface consistency, but some incorrect matches may be locally undetectable. A global optimization process searches a graph constructed from these potentially faulty pair-wise matches for a connected sub-graph containing only correct matches, employing a global consistency measure to detect incorrect, but locally consistent matches. From this sub-graph, the final poses of all views can be computed directly. We apply our algorithm to the problem of 3D digital reconstruction of real world objects and show results for a collection of automatically digitized objects.

In this dissertation, we develop techniques to fully automate the process of constructing a digital, three-dimensional (3D) model from a set of 3D views of a static scene obtained from unknown viewpoints. Since 3D sensors only capture the scene structure as seen from a single viewpoint, multiple views must be combined to create a complete model. Current modeling methods are limited in that they require measurement of the sensor viewpoints or manual registration of the views. We have developed an alternative approach that fully automates the modeling process without without any knowledge the sensor viewpoints and without manual intervention.

Accurate and robust registration of multiple threedimensional (3D) views is crucial for creating digital 3D models of real-world scenes. In this paper, we present a framework for evaluating the quality of model hypotheses during the registration phase. We use maximum likelihood estimation to learn a probabilistic model of registration success. This method provides a principled way to combine multiple measures of registration accuracy. Also, we describe a stochastic algorithm for robustly searching the large space of possible models for the best model hypothesis. This new approach can detect situations in which no solution exists, outputting a set of model parts if a single model using all the views cannot be found. We show results for a large collection of automatically modeled scenes and demonstrate that our algorithm works independently of scene size and the type of range sensor. This work is part of a system we have developed to automate the 3D modeling process for a set of 3D views obtained from unknown sensor viewpoints.

Range sensors, such as laser range finder and stereo vision systems, return point-samples of a scene. Typical point-sampled vision problems include registration, regularization and merging. We introduce a robust distance minimization approach to solving the three classes of problems.

The use of detailed geometric models is a critical factor for achieving realism in most computer graphics applications. In the past few years, we have observed an increasing demand for faithful representations of real scenes, primarily driven by applications whose goal is to create extremely realistic experiences by building virtual replicas of real environments. Potential uses of this technology include entertainment, training and simulation, special effects, forensic analysis, and remote walkthroughs. Creating models of real scenes is, however, a complex task for which the use of traditional modeling techniques is inappropriate. Aiming to simplify the modeling and rendering tasks, several image-based techniques have been proposed in recent years. Among these, the combined use of laser rangefinders and color images appears as one the most promising approaches due to its relative independence of the sampled geometry and short acquisition time. Renderings of scenes modeled with such a technique can potentially exhibit an unprecedented degree of photorealism. But before one can actually render new views of these virtualized environments, several challenges need to be addressed. This tutorial provides an overview of the main issues associated with the modeling and rendering of real environments sampled with laser rangefinders, and discusses the main techniques used to address these challenges.

This thesis is concerned with three-dimensional scan registration, in particular of underground mine tunnels. Registration of partial range scans is an essential part of building 3D maps, and autonomous creation of reliable maps is a first step towards autonomous mining vehicles. The thesis presents a survey of relevant sensor technology and discusses the advantages and disadvantages of different sensors for use in mine environments. A survey of the state of the art in scan registration algorithms is also presented, as well as a number of relevant applications. A new algorithm for registration of 3D data is presented, which is a generalisation of the normal distributions transform (NDT) for 2D data developed by Biber and Straßer. A detailed quantitative and qualitative comparison of the new algorithm with existing registration algorithms is shown. Results with actual mine data, some of which were collected with a new prototype 3D laser scanner, show that the presented algorithm is faster and in many cases more