Surface reconstruction from unorganized points

By Hugues Hoppe, Tony DeRose, T. Duchamp, J. McDonald, Werner Stuetzle. 1992

We describe and demonstrate an algorithm that takes as input an unorganized set of points {xl, . . . . xn} ⊂ R3 on or near an unknown manifold M, and produces as output a simplicial surface that approximates M. Neither the topology, the presence of boundaries, nor the geometry of M are assumed to be known in advance - all are inferred automatically from the data. This problem naturally arises in a variety of practical situations such as range scanning an object from multiple view points, recovery of biological shapes from two-dimensional slices, and interactive surface sketching.

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

Citations in the corpus

PCPNet Learning Local Shape Properties from Raw Point Clouds (2017, methodology): “This method, used as early as [HDD ∗ 92], is based on analyzing the variance in a patch around a point and reporting the direction of minimal variance as the normal.”
Voronoi-based variational reconstruction of unoriented point sets (2007, unknown): “Although various expressions of the anisotropic Dirichlet energy have been proposed in 2D in the context of quasiharmonic parameterizations [ Gus02 ,ZRS05], we construct our 3D energies via matrix assembly using the language of discrete forms [DKT06].”
Real-Time 3D Reconstruction in Dynamic Scenes Using Point-Based Fusion (2013, background): “Unlike methods that focus on reconstruction from a complete set of 3D points [5, 7], online methods require fusion of many overlapping depth maps into a single 3D representation that is continuously refined.”
EFFICIENT HOUGH TRANSFORM FOR AUTOMATIC DETECTION OF CYLINDERS IN POINT CLOUDS (2005, methodology): “For more details see (Hoppe et al., 1992).”
Automated dimensional quality assessment of precast concrete panels using terrestrial laser scanning (2014, background): “The main purpose of these approaches is to improve surface reconstruction of an object based on dense laser scan data [17,3,10].”
Point set surfaces (2001, methodology): “Criteria include the distance of points on the surface [18], curvature [12], or distance from the medial axis of the shape [1].”
OctoMap: an efficient probabilistic 3D mapping framework based on octrees (2013, background): “It can also be used as a starting point to create colored, high-resolution surface meshes (Hoppe et al., 1992).”
Aligning point cloud views using persistent feature histograms (2008, background): “see [24] for a general algorithm for consistent normal orientation propagation for 3D objects Fig.”
Edge-aware point set resampling (2013, methodology): “The popular PCA-based method for computing surface normal approximations from point cloud data [Hoppe et al. 1992] uses local point neighborhoods.”
Surface splatting (2001, unknown): “A commonly used approach is generating triangle meshes from the point data and using mesh reduction techniques to render them [7, 2].”
Direct visibility of point sets (2007, methodology): “Different approaches have been proposed, such as representations by implicit functions [Hoppe et al. 1992; Carr et al. 2001; Ohtake et al. 2003], by Moving Least Squares [Alexa et al. 2003; Fleishman et al. 2005], employing Voronoi/Delaunay techniques [Amenta et al. 2002; Amenta et al. 2001;…”
A Level-Set Approach to 3D Reconstruction from Range Data (1998, methodology): “Indeed, the algorithms proposed by (Curless and Levoy, 1996), as well as (Hoppe et al., 1992) and (Hilton et al.”
Feature sensitive surface extraction from volume data (2001, methodology): “One well-established technique to reconstruct a polygonal mesh model from an unstructured cloud of points is to estimate a signed distance function and then apply the Marching Cubes algorithm [7, 17].”
A quasi-dense approach to surface reconstruction from uncalibrated images (2005, methodology): “For densely scanned 3D point data, Szeliski and Weiss [51] used a particle-based model of deformable surfaces; Hoppe et al. [22] presented a signed distance for implicit surfaces; Curless and Levoy [7] described a volumetric method; and Tang and Medioni [52] introduced a tensor voting method.”