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80
Feature preserving point set surfaces based on nonlinear kernel regression
, 2009
"... Moving least squares (MLS) is a very attractive tool to design effective meshless surface representations. However, as long as approximations are performed in a least square sense, the resulting definitions remain sensitive to outliers, and smoothout small or sharp features. In this paper, we addre ..."
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Cited by 55 (3 self)
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Moving least squares (MLS) is a very attractive tool to design effective meshless surface representations. However, as long as approximations are performed in a least square sense, the resulting definitions remain sensitive to outliers, and smoothout small or sharp features. In this paper, we address these major issues, and present a novel point based surface definition combining the simplicity of implicit MLS surfaces [SOS04,Kol05] with the strength of robust statistics. To reach this new definition, we review MLS surfaces in terms of local kernel regression, opening the doors to a vast and well established literature from which we utilize robust kernel regression. Our novel representation can handle sparse sampling, generates a continuous surface better preserving fine details, and can naturally handle any kind of sharp features with controllable sharpness. Finally, it combines ease of implementation with performance competing with other nonrobust approaches.
Globfit: Consistently fitting primitives by discovering global relations
 ACM Trans. on Graphics
"... Figure 1: Starting from a noisy scan, our algorithm recovers the primitive faces along with their global mutual relations, when are then used to produce a final model (all lengths in mm). ..."
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Cited by 42 (12 self)
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Figure 1: Starting from a noisy scan, our algorithm recovers the primitive faces along with their global mutual relations, when are then used to produce a final model (all lengths in mm).
Dynamic sampling and rendering of algebraic point set surfaces
 COMPUTER GRAPHICS FORUM
, 2008
"... Algebraic Point Set Surfaces (APSS) define a smooth surface from a set of points using local moving leastsquares (MLS) fitting of algebraic spheres. In this paper we first revisit the spherical fitting problem and provide a new, more generic solution that includes intuitive parameters for curvature ..."
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Cited by 26 (10 self)
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Algebraic Point Set Surfaces (APSS) define a smooth surface from a set of points using local moving leastsquares (MLS) fitting of algebraic spheres. In this paper we first revisit the spherical fitting problem and provide a new, more generic solution that includes intuitive parameters for curvature control of the fitted spheres. As a second contribution we present a novel realtime rendering system of such surfaces using a dynamic upsampling strategy combined with a conventional splatting algorithm for high quality rendering. Our approach also includes a new view dependent geometric error tailored to efficient and adaptive upsampling of the surface. One of the key features of our system is its high degree of flexibility that enables us to achieve high performance even for highly dynamic data or complex models by exploiting temporal coherence at the primitive level. We also address the issue of efficient spatial search data structures with respect to construction, access and GPU friendliness. Finally, we present an efficient parallel GPU implementation of the algorithms and search structures.
Asprojectiveaspossible image stitching with moving DLT
 In Proceedings of IEEE CVPR 2013
, 2013
"... We investigate projective estimation under model inadequacies, i.e., when the underpinning assumptions of the projective model are not fully satisfied by the data. We focus on the task of image stitching which is customarily solved by estimating a projective warp — a model that is justified when t ..."
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Cited by 12 (1 self)
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We investigate projective estimation under model inadequacies, i.e., when the underpinning assumptions of the projective model are not fully satisfied by the data. We focus on the task of image stitching which is customarily solved by estimating a projective warp — a model that is justified when the scene is planar or when the views differ purely by rotation. Such conditions are easily violated in practice, and this yields stitching results with ghosting artefacts that necessitate the usage of deghosting algorithms. To this end we propose asprojectiveaspossible warps, i.e., warps that aim to be globally projective, yet allow local nonprojective deviations to account for violations to the assumed imaging conditions. Based on a novel estimation technique called Moving Direct Linear Transformation (Moving DLT), our method seamlessly bridges image regions that are inconsistent with the projective model. The result is highly accurate image stitching, with significantly reduced ghosting effects, thus lowering the dependency on post hoc deghosting. 1.
Feature preserving mesh generation from 3d point clouds
 Computer Graphics Forum
"... We address the problem of generating quality surface triangle meshes from 3D point clouds sampled on piecewise smooth surfaces. Using a feature detection process based on the covariance matrices of Voronoi cells, we first extract from the point cloud a set of sharp features. Our algorithm also runs ..."
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Cited by 11 (0 self)
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We address the problem of generating quality surface triangle meshes from 3D point clouds sampled on piecewise smooth surfaces. Using a feature detection process based on the covariance matrices of Voronoi cells, we first extract from the point cloud a set of sharp features. Our algorithm also runs on the input point cloud a reconstruction process, such as Poisson reconstruction, providing an implicit surface. A feature preserving variant of a Delaunay refinement process is then used to generate a mesh approximating the implicit surface and containing a faithful representation of the extracted sharp edges. Such a mesh provides an enhanced tradeoff between accuracy and mesh complexity. The whole process is robust to noise and made versatile through a small set of parameters which govern the mesh sizing, approximation error and shape of the elements. We demonstrate the effectiveness of our method on a variety of models including laser scanned datasets ranging from indoor to outdoor scenes. Categories and Subject Descriptors (according to ACM CCS): [Computational Geometry and Object Modeling] [I.3.5]: Curve, surface, solid, and object representations—
PatchGraph Reconstruction for Piecewise Smooth Surfaces
 VMV 2008
, 2008
"... In this paper we present a new surface reconstruction technique for piecewise smooth surfaces from point clouds, such as scans of architectural sites or manmade artifacts. The technique operates in three conceptual steps: First, a graph of local surface patches, each consisting of a set of basis fu ..."
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Cited by 10 (0 self)
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In this paper we present a new surface reconstruction technique for piecewise smooth surfaces from point clouds, such as scans of architectural sites or manmade artifacts. The technique operates in three conceptual steps: First, a graph of local surface patches, each consisting of a set of basis functions, is assembled. Second, we establish topological connectivity among the nodes that respects sharp features. Third, we find optimal coefficients for the basis functions in each node by solving a sparse optimization problem. Our final representation allows for robust finding of crease and border edges which separate the piecewise smooth parts. As output of our approach, we extract a clean, manifold surface mesh which preserves and even aggravates feature lines. The main benefit of our new proposal in comparison to previous work is its robustness and efficiency, which we examine by applying the algorithm to a variety of different synthetic and realword data sets.
State of the Art in Surface Reconstruction from Point Clouds
 IN PROC. EUROGRAPHICS 2014
, 2014
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An EndtoEnd Framework for Evaluating Surface Reconstruction
, 2011
"... We present a benchmark for the evaluation and comparison of algorithms which reconstruct a surface from point cloud data. Although a substantial amount of effort has been dedicated to the problem of surface reconstruction, a comprehensive means of evaluating this class of algorithms is noticeably ab ..."
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Cited by 8 (1 self)
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We present a benchmark for the evaluation and comparison of algorithms which reconstruct a surface from point cloud data. Although a substantial amount of effort has been dedicated to the problem of surface reconstruction, a comprehensive means of evaluating this class of algorithms is noticeably absent. We propose a simple pipeline for measuring surface reconstruction algorithms, consisting of three main phases: surface modeling, sampling, and evaluation.We employ implicit surfaces for modeling shapes which are expressive enough to contain details of varying size, in addition to preserving sharp features. From these implicit surfaces, we produce point clouds by synthetically generating range scans which resemble realistic scan data. We validate our synthetic sampling scheme by comparing against scan data produced via a commercial optical laser scanner, wherein we scan a 3Dprinted version of the original implicit surface. Last, we perform evaluation by comparing the output reconstructed surface to a dense uniformlydistributed sampling of the implicit surface. We decompose our benchmark into two distinct sets of experiments. The first set of experiments measures reconstruction against point clouds of complex shapes sampled under a wide variety of conditions. Although these experiments are quite useful for the comparison of surface reconstruction algorithms, they lack a finegrain analysis. Hence to complement this, the second set of experiments are designed to measure specific properties of surface reconstruction, both from a sampling and surface modeling viewpoint. Together, these experiments depict a detailed examination of the state of surface reconstruction algorithms.
A Survey of Methods for Moving Least Squares Surfaces
, 2008
"... Moving least squares (MLS) surfaces representation directly defines smooth surfaces from point cloud data, on which the differential geometric properties of point set can be conveniently estimated. Nowadays, the MLS surfaces have been widely applied in the processing and rendering of pointsampled m ..."
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Cited by 8 (0 self)
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Moving least squares (MLS) surfaces representation directly defines smooth surfaces from point cloud data, on which the differential geometric properties of point set can be conveniently estimated. Nowadays, the MLS surfaces have been widely applied in the processing and rendering of pointsampled models and increasingly adopted as the standard definition of point set surfaces. We classify the MLS surface algorithms into two types: projection MLS surfaces and implicit MLS surfaces, according to employing a stationary projection or a scalar field in their definitions. Then, the properties and constrains of the MLS surfaces are analyzed. After presenting its applications, we summarize the MLS surfaces definitions in a generic form and give the outlook of the future work at last.