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259
Consolidation of Unorganized Point Clouds for Surface Reconstruction
"... We consolidate an unorganized point cloud with noise, outliers, nonuniformities, and in particular interference between closeby surface sheets as a preprocess to surface generation, focusing on reliable normal estimation. Our algorithm includes two new developments. First, a weighted locally optim ..."
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Cited by 47 (10 self)
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We consolidate an unorganized point cloud with noise, outliers, nonuniformities, and in particular interference between closeby surface sheets as a preprocess to surface generation, focusing on reliable normal estimation. Our algorithm includes two new developments. First, a weighted locally optimal projection operator produces a set of denoised, outlierfree and evenly distributed particles over the original dense point cloud, so as to improve the reliability of local PCA for initial estimate of normals. Next, an iterative framework for robust normal estimation is introduced, where a prioritydriven normal propagation scheme based on a new priority measure and an orientationaware PCA work complementarily and iteratively to consolidate particle normals. The priority setting is reinforced with front stopping at thin surface features and normal flipping to enable robust handling of the closeby surface sheet problem. We demonstrate how a point cloud that is wellconsolidated by our method steers conventional surface generation schemes towards a proper interpretation of the input data. 1
Robust Reconstruction of Watertight 3D Models from Nonuniformly Sampled Point Clouds Without Normal Information
, 2006
"... We present a new volumetric method for reconstructing watertight triangle meshes from arbitrary, unoriented point clouds. While previous techniques usually reconstruct surfaces as the zero levelset of a signed distance function, our method uses an unsigned distance function and hence does not requi ..."
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Cited by 46 (0 self)
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We present a new volumetric method for reconstructing watertight triangle meshes from arbitrary, unoriented point clouds. While previous techniques usually reconstruct surfaces as the zero levelset of a signed distance function, our method uses an unsigned distance function and hence does not require any information about the local surface orientation. Our algorithm estimates local surface confidence values within a dilated crust around the input samples. The surface which maximizes the global confidence is then extracted by computing the minimum cut of a weighted spatial graph structure. We present an algorithm, which efficiently converts this cut into a closed, manifold triangle mesh with a minimal number of vertices. The use of an unsigned distance function avoids the topological noise artifacts caused by misalignment of 3D scans, which are common to most volumetric reconstruction techniques. Due to a hierarchical approach our method efficiently produces solid models of low genus even for noisy and highly irregular data containing large holes, without loosing fine details in densely sampled regions. We show several examples for different application settings such as model generation from raw laserscanned data, imagebased 3D reconstruction, and mesh repair.
SmoothSurface Reconstruction in Near Linear Time
, 2001
"... A surface reconstruction algorithm takes as input a set of sample points from an unknown closed and smooth surface in 3d space, and produces a piecewise linear approximation of the surface that contains the sample points. Variants of this problem have received considerable attention in computer ..."
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Cited by 41 (5 self)
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A surface reconstruction algorithm takes as input a set of sample points from an unknown closed and smooth surface in 3d space, and produces a piecewise linear approximation of the surface that contains the sample points. Variants of this problem have received considerable attention in computer vision and computer graphics and more recently in computational geometry. In the latter area, three different algorithms (Amenta and Bern `98, and refined in Amenta, Choi, Dey and Leekha `00; Amenta, Choi and Kolluri `00; Boissonnat and Cazals `00) have been proposed. These algorithms have a correctness guarantee: if the sample is sufficiently dense then the output is a good approximation to the original surface. They have unfortunately a worstcase running time that is quadratic in the size of the input. This is so because they are based on the construction of 3d Voronoi diagrams or Delaunay tetrahedrizations, which can have quadratic size. Even worse, according to recent work (Erickson `01), there are surfaces for which this is the case even when the sample set is "locally uniform" on the surface. In this paper, we describe a new algorithm that also has a correctness guarantee but whose worstcase running time is almost linear. In fact, O(n log n) where n is the input size. As in some of the previous algorithms, the piecewise linear approximation produced by the new algorithm is a subset of the 3d Delaunay tetrahedrization; however, this is obtained by computing only the relevant parts of the 3d Delaunay structure. The algorithm first estimates for each sample point the surface normal and a parameter that is then used to "decimate" the set of samples. The resulting subset of sample points is locally uniform and so a reconstruction based on it can be compu...
Fast and high quality fusion of depth maps
 In 3DPVT
, 2008
"... Reconstructing the 3D surface from a set of provided range images – acquired by active or passive sensors – is an important step to generate faithful virtual models of real objects or environments. Since several approaches for high quality fusion of range images are already known, the runtime effic ..."
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Cited by 41 (1 self)
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Reconstructing the 3D surface from a set of provided range images – acquired by active or passive sensors – is an important step to generate faithful virtual models of real objects or environments. Since several approaches for high quality fusion of range images are already known, the runtime efficiency of the respective methods are of increased interest. In this paper we propose a highly efficient method for range image fusion resulting in very accurate 3D models. We employ a variational formulation for the surface reconstruction task. The global optimal solution can be found by gradient descent due to the convexity of the underlying energy functional. Further, the gradient descent procedure can be parallelized, and consequently accelerated by graphics processing units. The quality and runtime performance of the proposed method is demonstrated on wellknown multiview stereo benchmark datasets. 1.
3D object retrieval using manytomany matching of curve skeletons
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, 2005
"... We present a 3D matching framework based on a manytomany matching algorithm that works with skeletal representations of 3D volumetric objects. We demonstrate the performance of this approach on a large database of 3D objects containing more than 1000 exemplars. The method is especially suited t ..."
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Cited by 40 (3 self)
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We present a 3D matching framework based on a manytomany matching algorithm that works with skeletal representations of 3D volumetric objects. We demonstrate the performance of this approach on a large database of 3D objects containing more than 1000 exemplars. The method is especially suited to matching objects with distinct part structure and is invariant to part articulation. Skeletal matching has an intuitive quality that helps in defining the search and visualizing the results. In particular, the matching algorithm produces a direct correspondence between two skeletons and their parts, which can be used for registration and juxtaposition. 1.
Finding narrow passages with probabilistic roadmaps: The small step retraction method
 in Proc. IEEE/RSJ Int. Conf. on Intelligent Robots & Systems
, 2005
"... Abstract: Probabilistic Roadmaps (PRM) have been successfully used to plan complex robot motions in configuration spaces of small and large dimensionalities. However, their efficiency decreases dramatically in spaces with narrow passages. This paper presents a new method – smallstep retraction – tha ..."
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Cited by 36 (3 self)
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Abstract: Probabilistic Roadmaps (PRM) have been successfully used to plan complex robot motions in configuration spaces of small and large dimensionalities. However, their efficiency decreases dramatically in spaces with narrow passages. This paper presents a new method – smallstep retraction – that helps PRM planners find paths through such passages. This method consists of slightly “fattening ” robot’s free space, constructing a roadmap in fattened free space, and finally repairing portions of this roadmap by retracting them out of collision into actual free space. Fattened free space is not explicitly computed. Instead, the geometric models of workspace objects (robot links and/or obstacles) are “thinned ” around their medial axis. A robot configuration lies in fattened free space if the thinned objects do not collide at this configuration. Two repair strategies are proposed. The “optimist ” strategy waits until a complete path has been found in fattened free space before repairing it. Instead, the “pessimist ” strategy repairs the roadmap as it is being built. The former is usually very fast, but may fail in some pathological cases. The latter is more reliable, but not as fast. A simple combination of the two strategies yields an integrated planner that is both fast and reliable. This planner was implemented as an extension of a preexisting singlequery PRM planner. Comparative tests show that it is significantly faster (sometimes by several orders of magnitude) than the preexisting planner. 1.
On Fast Surface Reconstruction Methods for Large and Noisy Datasets
 in Proceedings of the IEEE International Conference on Robotics and Automation (ICRA
, 2009
"... Abstract — In this paper we present a method for fast surface reconstruction from large noisy datasets. Given an unorganized 3D point cloud, our algorithm recreates the underlying surface’s geometrical properties using data resampling and a robust triangulation algorithm in near realtime. For result ..."
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Cited by 34 (9 self)
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Abstract — In this paper we present a method for fast surface reconstruction from large noisy datasets. Given an unorganized 3D point cloud, our algorithm recreates the underlying surface’s geometrical properties using data resampling and a robust triangulation algorithm in near realtime. For resulting smooth surfaces, the data is resampled with variable densities according to previously estimated surface curvatures. Incremental scans are easily incorporated into an existing surface mesh, by determining the respective overlapping area and reconstructing only the updated part of the surface mesh. The proposed framework is flexible enough to be integrated with additional point label information, where groups of points sharing the same label are clustered together and can be reconstructed separately, thus allowing fast updates via triangular mesh decoupling. To validate our approach, we present results obtained from laser scans acquired in both indoor and outdoor environments. I.
Onthefly curveskeleton computation for 3d shapes
 Comput. Graph. Forum
"... Abstract. The curveskeleton of a 3D object is an abstract geometrical and topological representation of its 3D shape. It maps the spatial relation of geometrically meaningful parts to a graph structure. Each arc of this graph represents a part of the object with roughly constant diameter or thickne ..."
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Cited by 31 (2 self)
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Abstract. The curveskeleton of a 3D object is an abstract geometrical and topological representation of its 3D shape. It maps the spatial relation of geometrically meaningful parts to a graph structure. Each arc of this graph represents a part of the object with roughly constant diameter or thickness, and approximates its centerline. This makes the curveskeleton suitable to describe and handle articulated objects such as characters for animation. We present an algorithm to extract such a skeleton onthefly, both from point clouds and polygonal meshes. The algorithm is based on a deformable model evolution that captures the object’s volumetric shape. The deformable model involves multiple competing fronts which evolve inside the object in a coarsetofine manner. We first track these fronts ’ centers, and then merge and filter the resulting arcs to obtain a curveskeleton of the object. The process inherits the robustness of the reconstruction technique, being able to cope with noisy input, intricate geometry and complex topology. It creates a natural segmentation of the object and computes a center curve for each segment while maintaining a full correspondence between the skeleton and the boundary of the object.
Triangulating Point Set Surfaces with Bounded Error
, 2005
"... We introduce an algorithm for constructing a highquality triangulation directly from Point Set Surfaces. Our algorithm requires no intermediate representation and no postprocessing of the output, and naturally handles noisy input data, typically in the form of a set of registered range scans. It c ..."
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Cited by 29 (6 self)
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We introduce an algorithm for constructing a highquality triangulation directly from Point Set Surfaces. Our algorithm requires no intermediate representation and no postprocessing of the output, and naturally handles noisy input data, typically in the form of a set of registered range scans. It creates a triangulation where triangle size respects the geometry of the surface rather than the sampling density of the range scans. Our technique does not require normal information, but still produces a consistent orientation of the triangles, assuming the sampled surface is an orientable twomanifold. Our work is based on using Moving LeastSquares (MLS) surfaces as the underlying representation. Our technique is a novel advancing front algorithm, that bounds the Hausdorff distance to within a userspecified limit. Specifically, we introduce a way of augmenting advancing front algorithms with global information, so that triangle size adapts gracefully even when there are large changes in surface curvature. Our results show that our technique generates highquality triangulations where other techniques fail to reconstruct the correct surface due to irregular sampling on the point cloud, noise, registration artifacts, and underlying geometric features, such as regions with high curvature gradients.
Computing a family of skeletons of volumetric models for shape description
 In Geometric Modeling and Processing
, 2007
"... Abstract. Skeletons are important shape descriptors in object representation and recognition. Typically, skeletons of volumetric models are computed via an iterative thinning process. However, traditional thinning methods often generate skeletons with complex structures that are unsuitable for shape ..."
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Cited by 29 (6 self)
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Abstract. Skeletons are important shape descriptors in object representation and recognition. Typically, skeletons of volumetric models are computed via an iterative thinning process. However, traditional thinning methods often generate skeletons with complex structures that are unsuitable for shape description, and appropriate pruning methods are lacking. In this paper, we present a new method for computing skeletons on volumes by alternating thinning and a novel skeleton pruning routine. Our method creates a family of skeletons parameterized by two userspecified numbers that determine respectively the size of curve and surface features on the skeleton. As demonstrated on both realworld models and medical images, our method generates skeletons with simple and meaningful structures that are particularly suitable for describing cylindrical and platelike shapes. 1