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654
Spinimages: A representation for 3d surface matching
, 1997
"... surface registration, object modeling, scene clutter. Dedicated to Dorothy D. Funnell, a believer in higher education. Surface matching is the process that compares surfaces and decides whether they are similar. In threedimensional (3D) computer vision, surface matching plays a prominent role. Sur ..."
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Cited by 166 (4 self)
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surface registration, object modeling, scene clutter. Dedicated to Dorothy D. Funnell, a believer in higher education. Surface matching is the process that compares surfaces and decides whether they are similar. In threedimensional (3D) computer vision, surface matching plays a prominent role. Surface matching can be used for object recognition; by comparing two surfaces, an association between a known object and sensed data is established. By computing the 3D transformation that aligns two surfaces, surface matching can also be used for surface registration. Surface matching is difficult because the coordinate system in which to compare two surfaces is undefined. The typical approach to surface matching is to transform the surfaces being compared into representations where comparison of surfaces is straightforward. Surface matching is further complicated by characteristics of sensed data, including clutter, occlusion and sensor noise. This thesis describes a data level representation of surfaces used for surface matching. In our representation, surface shape is described by a dense collection of oriented points, 3D
ThreeDimensional Face Recognition
, 2005
"... An expressioninvariant 3D face recognition approach is presented. Our basic assumption is that facial expressions can be modelled as isometries of the facial surface. This allows to construct expressioninvariant representations of faces using the bendinginvariant canonical forms approach. The re ..."
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Cited by 145 (24 self)
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An expressioninvariant 3D face recognition approach is presented. Our basic assumption is that facial expressions can be modelled as isometries of the facial surface. This allows to construct expressioninvariant representations of faces using the bendinginvariant canonical forms approach. The result is an efficient and accurate face recognition algorithm, robust to facial expressions, that can distinguish between identical twins (the first two authors). We demonstrate a prototype system based on the proposed algorithm and compare its performance to classical face recognition methods. The numerical methods employed by our approach do not require the facial surface explicitly. The surface gradients field, or the surface metric, are sufficient for constructing the expressioninvariant representation of any given face. It allows us to perform the 3D face recognition task while avoiding the surface reconstruction stage.
Fast Point Feature Histograms (FPFH) for 3D Registration
 in In Proceedings of the International Conference on Robotics and Automation (ICRA
, 2009
"... Abstract — In our recent work [1], [2], we proposed Point Feature Histograms (PFH) as robust multidimensional features which describe the local geometry around a point p for 3D point cloud datasets. In this paper, we modify their mathematical expressions and perform a rigorous analysis on their rob ..."
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Cited by 137 (6 self)
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Abstract — In our recent work [1], [2], we proposed Point Feature Histograms (PFH) as robust multidimensional features which describe the local geometry around a point p for 3D point cloud datasets. In this paper, we modify their mathematical expressions and perform a rigorous analysis on their robustness and complexity for the problem of 3D registration for overlapping point cloud views. More concretely, we present several optimizations that reduce their computation times drastically by either caching previously computed values or by revising their theoretical formulations. The latter results in a new type of local features, called Fast Point Feature Histograms (FPFH), which retain most of the discriminative power of the PFH. Moreover, we propose an algorithm for the online computation of FPFH features for realtime applications. To validate our results we demonstrate their efficiency for 3D registration and propose a new sample consensus based method for bringing two datasets into the convergence basin of a local nonlinear optimizer: SACIA (SAmple Consensus Initial Alignment). I.
Nonrigid point set registration: Coherent Point Drift (CPD)
 IN ADVANCES IN NEURAL INFORMATION PROCESSING SYSTEMS 19
, 2006
"... We introduce Coherent Point Drift (CPD), a novel probabilistic method for nonrigid registration of point sets. The registration is treated as a Maximum Likelihood (ML) estimation problem with motion coherence constraint over the velocity field such that one point set moves coherently to align with ..."
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Cited by 136 (0 self)
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We introduce Coherent Point Drift (CPD), a novel probabilistic method for nonrigid registration of point sets. The registration is treated as a Maximum Likelihood (ML) estimation problem with motion coherence constraint over the velocity field such that one point set moves coherently to align with the second set. We formulate the motion coherence constraint and derive a solution of regularized ML estimation through the variational approach, which leads to an elegant kernel form. We also derive the EM algorithm for the penalized ML optimization with deterministic annealing. The CPD method simultaneously finds both the nonrigid transformation and the correspondence between two point sets without making any prior assumption of the transformation model except that of motion coherence. This method can estimate complex nonlinear nonrigid transformations, and is shown to be accurate on 2D and 3D examples and robust in the presence of outliers and missing points.
ICP Registration using Invariant Features
, 2002
"... This paper investigates the use of Euclidean invariant features in a generalization of iterative closest point registration of range images. Pointwise correspondences are chosen as the closest point with respect to a weighted linear combination of positional and feature distances. It is shown that u ..."
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Cited by 124 (0 self)
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This paper investigates the use of Euclidean invariant features in a generalization of iterative closest point registration of range images. Pointwise correspondences are chosen as the closest point with respect to a weighted linear combination of positional and feature distances. It is shown that under ideal noisefree conditions, correspondences formed using this distance function are correct more often than correspondences formed using the positional distance alone. In addition, monotonic convergence to at least a local minimum is shown to hold for this method. When noise is present, a method that automatically sets the optimal relative contribution of features and positions is described. This method trades off error in feature values due to noise against error in positions due to misalignment. Experimental results suggest that using invariant features decreases the probability of being trapped in a local minimum, and may be an effective solution for difficult range image registration problems where the scene is very small compared to the model.
An autonomous mobile robot with a 3D laser range finder for 3D exploration and digitalization of indoor environments
, 2003
"... Digital 3D models of the environment are needed in rescue and inspection robotics, facility managements and architecture. This paper presents an automatic system for gaging and digitalization of 3D indoor environments. It consists of an autonomous mobile robot, a reliable 3D laser range finder and t ..."
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Cited by 116 (23 self)
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Digital 3D models of the environment are needed in rescue and inspection robotics, facility managements and architecture. This paper presents an automatic system for gaging and digitalization of 3D indoor environments. It consists of an autonomous mobile robot, a reliable 3D laser range finder and three elaborated software modules. The first module, a fast variant of the Iterative Closest Points algorithm, registers the 3D scans in a common coordinate system and relocalizes the robot. The second module, a next best view planner, computes the next nominal pose based on the acquired 3D data while avoiding complicated obstacles. The third module, a closedloop and globally stable motor controller, navigates the mobile robot to a nominal pose on the base of odometry and avoids collisions with dynamical obstacles. The 3D laser range finder acquires a 3D scan at this pose. The proposed method allows one to digitalize large indoor environments fast and reliably without any intervention and solves the SLAM problem. The results of two 3D digitalization experiments are presented using a fast octreebased visualization method.
Registration and Integration of Textured 3D Data
 IMAGE AND VISION COMPUTING
, 1996
"... In general, multiple views are required to create a complete 3D model of an object or a multiroomed indoor scene. In this work, we address the problem of merging multiple textured 3D data sets, each of which corresponding to a different view of a scene or object. There are two steps to the merging ..."
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Cited by 106 (3 self)
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In general, multiple views are required to create a complete 3D model of an object or a multiroomed indoor scene. In this work, we address the problem of merging multiple textured 3D data sets, each of which corresponding to a different view of a scene or object. There are two steps to the merging process: registration and integration. Registration is the process by which data sets are brought into alignment. To this end, we use a modified version of the Iterative Closest Point algorithm (ICP); our version, which we call color ICP, considers not only 3D information, but color as well. This has shown to have resulted in improved performance. Once the 3D data sets have been registered, we then integrate them to produce a seamless, composite 3D textured model. Our approach to integration uses a 3D occupancy grid to represent likelihood of spatial occupancy through voting. The occupancy grid representation allows the incorporation of sensor modeling. The surface of the merged model i...
ShapefromSilhouette Across Time  Part I: Theory and Algorithms
 International Journal of Computer Vision
, 2005
"... ShapeFromSilhouette (SFS) is a shape reconstruction method which constructs a 3D shape estimate of an object using silhouette images of the object. The output of a SFS algorithm is known as the Visual Hull (VH). Traditionally SFS is either performed on static objects, or separately at each time in ..."
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Cited by 105 (3 self)
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ShapeFromSilhouette (SFS) is a shape reconstruction method which constructs a 3D shape estimate of an object using silhouette images of the object. The output of a SFS algorithm is known as the Visual Hull (VH). Traditionally SFS is either performed on static objects, or separately at each time instant in the case of videos of moving objects. In this paper we develop a theory of performing SFS across time: estimating the shape of a dynamic object (with unknown motion) by combining all of the silhouette images of the object over time. We first introduce a one dimensional element called a Bounding Edge to represent the Visual Hull. We then show that aligning two Visual Hulls using just their silhouettes is in general ambiguous and derive the geometric constraints (in terms of Bounding Edges) that govern the alignment. To break the alignment ambiguity, we combine stereo information with silhouette information and derive a Temporal SFS algorithm which consists of two steps: (1) estimate the motion of the objects over time (Visual Hull Alignment) and (2) combine the silhouette information using the estimated motion (Visual Hull Refinement). The algorithm is first developed for rigid objects and then extended to articulated objects. In the Part II of this paper we apply our temporal SFS algorithm to two humanrelated applications: (1) the acquisition of detailed human kinematic models and (2) markerless motion tracking.
Fully Automatic Registration Of Multiple 3D Data Sets
, 2001
"... 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 ..."
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Cited by 98 (5 self)
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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 pairwise 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 pairwise matches for a connected subgraph containing only correct matches, employing a global consistency measure to detect incorrect, but locally consistent matches. From this subgraph, 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.
A Solution to the Simultaneous Localisation and Map Building (SLAM) Problem
"... The simultaneous localisation and map building (SLAM) problem asks if it is possible for an autonomous vehicle to start in an unknown location in an unknown environment and then to incrementally build a map of this environment while simultaneously using this map to compute absolute vehicle locatio ..."
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Cited by 94 (5 self)
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The simultaneous localisation and map building (SLAM) problem asks if it is possible for an autonomous vehicle to start in an unknown location in an unknown environment and then to incrementally build a map of this environment while simultaneously using this map to compute absolute vehicle location.Starting from the estimationtheoretic foundations of this problem developed in [1], [2], [3], this paper proves that a solution to the SLAM problem is indeed possible.The underlying structure of the SLAM problem is first elucidated.A proof that the estimated map converges monotonically to a relative map with zero uncertainty is then developed.It is then shown that the absolute accuracy of the map and the vehicle location reach a lower bound defined only by the initial vehicle uncertainty.Together, these results show that it is possible for an autonomous vehicle to start in an unknown location in an unknown environment and, using relative observations only, incrementally build a perfect map of the world and to compute simultaneously a bounded estimate of vehicle location. This paper also describes a substantial implementation of the SLAM algorithm on a vehicle operating in an outdoor environment using millimeterwave (MMW) radar to provide relative map observations.This implementation is used to demonstrate how some key issues such as map management and data association can be handled in a practical environment.The results obtained are crosscompared with absolute locations of the map landmarks obtained by surveying.In conclusion, this paper discusses a number of key issues raised by the solution to the SLAM problem including suboptimal mapbuilding algorithms and map management.