Results 1  10
of
55
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). ..."
Abstract

Cited by 42 (12 self)
 Add to MetaCart
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).
Nonlocal Scan Consolidation for 3D Urban Scenes
"... Recent advances in scanning technologies, in particular devices that extract depth through active sensing, allow fast scanning of urban scenes. Such rapid acquisition incurs imperfections: large regions remain missing, significant variation in sampling density is common, and the data is often corrup ..."
Abstract

Cited by 38 (11 self)
 Add to MetaCart
Recent advances in scanning technologies, in particular devices that extract depth through active sensing, allow fast scanning of urban scenes. Such rapid acquisition incurs imperfections: large regions remain missing, significant variation in sampling density is common, and the data is often corrupted with noise and outliers. However, buildings often exhibit large scale repetitions and selfsimilarities. Detecting, extracting, and utilizing such large scale repetitions provide powerful means to consolidate the imperfect data. Our key observation is that the same geometry, when scanned multiple times over reoccurrences of instances, allow application of a simple yet effective nonlocal filtering. The multiplicity of the geometry is fused together and projected to a basegeometry defined by clustering corresponding surfaces. Denoising is applied by separating the process into offplane and inplane phases. We show that the consolidation of the reoccurrences provides robust denoising and allow reliable completion of missing parts. We present evaluation results of the algorithm on several LiDAR scans of buildings of varying complexity and styles. 1
Analysis, Reconstruction and Manipulation using Arterial Snakes
"... metal stool scanned pointset skeletal snakes arterial snake network edited model Figure 1: Starting from a noisy raw scan with large parts missing our algorithm analyzes and extracts a curve network with associated crosssectional profiles providing a reconstructed model. The extracted highlevel sh ..."
Abstract

Cited by 17 (7 self)
 Add to MetaCart
metal stool scanned pointset skeletal snakes arterial snake network edited model Figure 1: Starting from a noisy raw scan with large parts missing our algorithm analyzes and extracts a curve network with associated crosssectional profiles providing a reconstructed model. The extracted highlevel shape representation enables easy, intuitive, yet powerful geometry editing. Note that our algorithm is targeted towards delicate 1D features and fails to detect the small disc at the top of the stool. Manmade objects often consist of detailed and interleaving structures, which are created using cane, coils, metal wires, rods, etc. The delicate structures, although manufactured using simple procedures, are challenging to scan and reconstruct. We observe that such structures are inherently 1D, and hence are naturally represented using an arrangement of generating curves. We refer to the resultant surfaces as arterial surfaces. In this paper we approach for analyzing, reconstructing, and manipulating such arterial surfaces. ∗ Corresponding authors:
Robust Voronoibased Curvature and Feature Estimation
 SIAM/ACM JOINT CONFERENCE ON GEOMETRIC AND PHYSICAL MODELING
, 2009
"... Many algorithms for shape analysis and shape processing rely on accurate estimates of di erential information such as normals and curvature. In most settings, however, care must be taken around nonsmooth areas of the shape where these quantities are not easily de ned. This problem is particularly pr ..."
Abstract

Cited by 16 (3 self)
 Add to MetaCart
Many algorithms for shape analysis and shape processing rely on accurate estimates of di erential information such as normals and curvature. In most settings, however, care must be taken around nonsmooth areas of the shape where these quantities are not easily de ned. This problem is particularly prominent with pointcloud data, which are discontinuous everywhere. In this paper we present an e cient and robust method for extracting principal curvatures, sharp features and normal directions of a piecewise smooth surface from its point cloud sampling, with theoretical guarantees. Our method is integral in nature and uses convolved covariance matrices of Voronoi cells of the point cloud which makes it provably robust in the presence of noise. We show analytically that our method recovers correct principal curvatures and principal curvature directions in smooth parts of the shape, and correct feature directions and feature angles at the sharp edges of a piecewise smooth surface, with the error bounded by the Hausdor distance between the point cloud and the underlying surface. Using the same analysis we provide theoretical guarantees for a modi cation of a previously proposed normal estimation technique. We illustrate the correctness of both principal curvature information and feature extraction in the presence of varying levels of noise and sampling density on a variety of models.
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 ..."
Abstract

Cited by 11 (0 self)
 Add to MetaCart
(Show Context)
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—
Sharp Feature Detection in Point Clouds
 IEEE INTERNATIONAL CONFERENCE ON SHAPE MODELING AND APPLICATIONS (SMI)
, 2010
"... This paper presents a new technique for detecting sharp features on pointsampled geometry. Sharp features of different nature and possessing angles varying from obtuse to acute can be identified without any user interaction. The algorithm works directly on the point cloud, no surface reconstructio ..."
Abstract

Cited by 10 (1 self)
 Add to MetaCart
This paper presents a new technique for detecting sharp features on pointsampled geometry. Sharp features of different nature and possessing angles varying from obtuse to acute can be identified without any user interaction. The algorithm works directly on the point cloud, no surface reconstruction is needed. Given an unstructured point cloud, our method first computes a Gauss map clustering on local neighborhoods in order to discard all points which are unlikely to belong to a sharp feature. As usual, a global sensitivity parameter is used in this stage. In a second stage, the remaining feature candidates undergo a more precise iterative selection process. Central to our method is the automatic computation of an adaptive sensitivity parameter, increasing significantly the reliability and making the identification more robust in the presence of obtuse and acute angles. The algorithm is fast and does not depend on the sampling resolution, since it is based on a local neighbor graph computation.
State of the Art in Surface Reconstruction from Point Clouds
 IN PROC. EUROGRAPHICS 2014
, 2014
"... ..."
(Show Context)
FeaturePreserving Surface Reconstruction and Simplification from DefectLaden Point Sets
, 2012
"... We introduce a robust and featurecapturing surface reconstruction and simplification method that turns an input point set into a low trianglecount simplicial complex. Our approach starts with a (possibly nonmanifold) simplicial complex filtered from a 3D Delaunay triangulation of the input point ..."
Abstract

Cited by 8 (1 self)
 Add to MetaCart
We introduce a robust and featurecapturing surface reconstruction and simplification method that turns an input point set into a low trianglecount simplicial complex. Our approach starts with a (possibly nonmanifold) simplicial complex filtered from a 3D Delaunay triangulation of the input points. This initial approximation is iteratively simplified based on an error metric that measures, through optimal transport, the distance between the input points and the current simplicial complex—both seen as mass distributions. Our approach is shown to exhibit both robustness to noise and outliers, as well as preservation of sharp features and boundaries. Our new featuresensitive metric between point sets and triangle meshes can also be used as a postprocessing tool that, from the smooth output of a reconstruction method, recovers sharp features and boundaries present in the initial point set.
1Sparse reconstruction of sharp point set surfaces
 ACM T. Graphic
"... We introduce an 1sparse method for the reconstruction of a piecewise smooth point set surface. The technique is motivated by recent advancements in sparse signal reconstruction. The assumption underlying our work is that common objects, even geometrically complex ones, can typically be characterize ..."
Abstract

Cited by 7 (1 self)
 Add to MetaCart
We introduce an 1sparse method for the reconstruction of a piecewise smooth point set surface. The technique is motivated by recent advancements in sparse signal reconstruction. The assumption underlying our work is that common objects, even geometrically complex ones, can typically be characterized by a rather small number of features. This, in turn, naturally lends itself to incorporating the powerful notion of sparsity into the model. The sparse reconstruction principle gives rise to a reconstructed point set surface that consists mainly of smooth modes, with the residual of the objective function strongly concentrated near sharp features. Our technique is capable of recovering orientation and positions of highly noisy point sets. The global nature of the optimization yields a sparse solution and avoids local minima. Using an interiorpoint logbarrier solver with a customized preconditioning scheme, the solver for the corresponding convex optimization problem is competitive and the results are of high quality.