In this paper, we describe a system for the reconstruction of deforming geometry from a time sequence of unstructured, noisy point clouds, as produced by recent real-time range scanning devices. Our technique reconstructs both the geometry and dense correspondences over time. Using the correspondences, holes due to occlusion are filled in from other frames. Our reconstruction technique is based on a statistical framework: The reconstruction should both match the measured data points and maximize prior probability densities that prefer smoothness, rigid deformation and smooth movements over time. The optimization procedure consists of an inner loop that optimizes the 4D shape using continuous numerical optimization and an outer loop that infers the discrete 4D topology of the data set using an iterative model assembly algorithm. We apply the technique to a variety of data sets, demonstrating that the new approach is capable of robustly retrieving animated models with correspondences from data sets suffering from significant noise, outliers and acquisition holes.

Efficient and comfortable acquisition of large 3D scenes is an important topic for many current and future applications like cultural heritage, web applications and 3DTV and therefore it is a hot research topic. In this paper we present a new mobile 3D model acquisition platform. The platform uses 2D laser range scanners for both self localization by scan matching and geometry acquisition and a digital panorama camera. 3D models are acquired just by moving the platform around. Thereby, geometry is acquired continuously and color images are taken in regular intervals. After matching, the geometry is represented as unstructured point cloud which can then be rendered in several ways, for example using splatting with view dependent texturing. The work presented here is still “in progress”, but we are able to present some first reconstruction results of indoor and outdoor scenes.

For a mobile robot it is a natural task to build a 3D model of its environment. Such a model is not only useful for planning robot actions but also to provide a remote human surveillant a realistic visualization of the robot's state with respect to the environment. Acquiring 3D models of environments is also an important task on its own with many possible applications like creating virtual interactive walkthroughs or as basis for 3D-TV. In this paper we present our method to acquire a 3D model using a mobile robot that is equipped with a laser scanner and a panoramic camera. The method is based on calculating dense depth maps for panoramic images using pairs of panoramic images taken from different positions using stereo matching. Traditional 2D-SLAM using laser-scan-matching is used to determine the needed camera poses. To receive high-quality results we use a high-quality stereo matching algorithm -- the graph cut method. We describe the necessary modifications to handle panoramic images and specialized post-processing methods.

Transform (3D-NDT) is a spatial modeling technique with applications in point set registration, scan similarity comparison, change detection and path planning. This work concentrates on evaluating three common variations of the 3D-NDT in terms of accuracy of representing sampled semi-structured environments. In a novel approach to spatial representation quality measurement, the 3D geometrical modeling task is formulated as a classification problem and its accuracy is evaluated with standard machine learning performance metrics. In this manner the accuracy of the 3D-NDT variations is shown to be comparable to, and in some cases to outperform that of the standard occupancy grid mapping model. I.

This document is a draft. 10th November 2005

Abstract — In this paper we combine the Iterative Closest Point (’ICP’) and ‘point-to-plane ICP ‘ algorithms into a single probabilistic framework. We then use this framework to model locally planar surface structure from both scans instead of just the ”model ” scan as is typically done with the point-to-plane method. This can be thought of as ‘plane-to-plane’. The new approach is tested with both simulated and real-world data and is shown to outperform both standard ICP and point-to-plane. Furthermore, the new approach is shown to be more robust to incorrect correspondences, and thus makes it easier to tune the maximum match distance parameter present in most variants of ICP. In addition to the demonstrated performance improvement, the proposed model allows for more expressive probabilistic models to be incorporated into the ICP framework. While maintaining the speed and simplicity of ICP, the Generalized-ICP could also allow for the addition of outlier terms, measurement noise, and other probabilistic techniques to increase robustness. I.

Future 3D Television critically relies on mechanisms for automatically acquiring and visualizing high quality 3D content of both indoor and outdoor scenes. The envisioned goal is that a photo-realistic 3D real-time rendering from the actual and potentially arbitrary viewpoint of the beholder who is watching 3DTV becomes possible. Such scenes include movie sets in studios, e.g., for talk shows, TV series and blockbuster movies, but also outdoor scenes, e.g., buildings in a neighborhood for a car chase or cultural heritage sites for a documentary. The goal of 3D model acquisition is to provide the 3D background models where potential 3D actors can be embedded. We present both the 3D acquisition and semi-immersive 3D visualization to give an impression how a future 3D Television system could be like. Index Terms- TV, Stereo vision, Laser radar, Machine vision 1.

Abstract — 3D range sensing is one of the important topics in robotics, as it is often a component in vital autonomous subsystems like collision avoidance, mapping and semantic perception. The development of affordable, high frame rate and precise 3D range sensors is thus of considerable interest. Recent advances in sensing technology have produced several novel sensors that attempt to meet these requirements. This work is concerned with the development of a holistic method for accuracy evaluation of the measurements produced by such devices. A method for comparison of range sensor output to a set of reference distance measurements is proposed. The approach is then used to compare the behavior of three integrated range sensing devices, to that of a standard actuated laser range sensor. Test cases in an uncontrolled indoor environment are performed in order to evaluate the sensors ’ performance in a challenging, realistic application scenario. I.

Abstract — Point set registration — the task of finding the best fitting alignment between two sets of point samples, is an important problem in mobile robotics. This article proposes a novel registration algorithm, based on the distance between Three-Dimensional Normal Distributions Transforms. 3D-NDT models — a sub-class of Gaussian Mixture Models with uniformly weighted, largely disjoint components, can be quickly computed from range point data. The proposed algorithm constructs 3D-NDT representations of the input point sets and then formulates an objective function based on the L2 distance between the considered models. Analytic first and second order derivatives of the objective function are computed and used in a standard Newton method optimization scheme, to obtain the best-fitting transformation. The proposed algorithm is evaluated and shown to be more accurate and faster, compared to a state of the art implementation of the Iterative Closest Point and 3D-NDT Point-to-Distribution algorithms.

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