Completion and Reconstruction with Primitive Shapes

Schnabel, Ruwen; Degener, Patrick; Klein, Reinhard

doi:10.1111/j.1467-8659.2009.01389.x

Cited by 121 publications

(99 citation statements)

References 38 publications

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“…On the other hand, 3D HT resulted slower and more sensitive to the segmentation parameters values. A popular strategy in reverse engineering involves locally fitting primitives like planes, cylinders, cones using RANSAC based methods (Schnabel et al 2009). Chen et al (2014) has proposed a modified RANSAC segmentation algorithm that is less sensitive to noise, maintains topological consistency, and avoids over and under-segmentation of building primitives, through the localized sampling to segment the polyhedral rooftop primitives and then through the application of a region growing based triangulated irregular network (TIN) to separate the coplanar primitives.…”

Section: Segmentation By Model Fittingmentioning

confidence: 99%

A Review Of Point Clouds Segmentation and Classification Algorithms

Grilli

Menna

Remondino

2017

Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci.

314

138

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ABSTRACT:Today 3D models and point clouds are very popular being currently used in several fields, shared through the internet and even accessed on mobile phones. Despite their broad availability, there is still a relevant need of methods, preferably automatic, to provide 3D data with meaningful attributes that characterize and provide significance to the objects represented in 3D. Segmentation is the process of grouping point clouds into multiple homogeneous regions with similar properties whereas classification is the step that labels these regions. The main goal of this paper is to analyse the most popular methodologies and algorithms to segment and classify 3D point clouds. Strong and weak points of the different solutions presented in literature or implemented in commercial software will be listed and shortly explained. For some algorithms, the results of the segmentation and classification is shown using real examples at different scale in the Cultural Heritage field. Finally, open issues and research topics will be discussed.

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Section: Segmentation By Model Fittingmentioning

confidence: 99%

A Review Of Point Clouds Segmentation and Classification Algorithms

Grilli

Menna

Remondino

2017

Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci.

314

138

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“…Our solution bears some resemblance to primitive fitting used in the context of structure recovery [Wu and Kobbelt 2005;Li et al 2011a], surface reconstruction [Gal et al 2007a;Schnabel et al 2009] or 3D collage assembly [Gal et al 2007b;Theobalt et al 2007]. However, the input to these techniques is either complete polygonal meshes [Wu and Kobbelt 2005;Gal et al 2007b] or dense but noisy point clouds with few holes of big size [Gal et al 2007a;Schnabel et al 2009;Li et al 2011a].…”

Section: Related Workmentioning

confidence: 99%

Structure recovery by part assembly

et al. 2012

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Figure 1: Given single-view scans by the Kinect system, containing highly noisy and incomplete 3D scans (upper left) and corresponding RGB images (lower left), our approach is able to faithfully recover their underlying structures (yellow) by assembling suitable parts (red) in the repository models (blue). AbstractThis paper presents a technique that allows quick conversion of acquired low-quality data from consumer-level scanning devices to high-quality 3D models with labeled semantic parts and meanwhile their assembly reasonably close to the underlying geometry. This is achieved by a novel structure recovery approach that is essentially local to global and bottom up, enabling the creation of new structures by assembling existing labeled parts with respect to the acquired data. We demonstrate that using only a small-scale shape repository, our part assembly approach is able to faithfully recover a variety of high-level structures from only a single-view scan of man-made objects acquired by the Kinect system, containing a highly noisy, incomplete 3D point cloud and a corresponding RGB image. Links:DL PDF WEB

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“…Schnabel et al [20] have used these surfaces to reconstruct the object as a manifold. They use a version of marching cubes [12] to reconstruct the manifold.…”

Section: Related Workmentioning

confidence: 99%

On the shape of a set of points and lines in the plane

Kreveld

Lankveld

Veltkamp

2011

Computer Graphics Forum

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Detailed geometric models of the real world are in increasing demand. LiDAR data is appropriate to reconstruct such models. For urban reconstruction, it works well to reconstruct the individual surfaces of the scene and connect them to form the scene geometry. There are various methods for reconstructing the free-form shape of a point sample on a single surface. However, these methods do not take the context of the surface into account. We present the generalized and guided α-shape: two extensions of the well known α-shape. The generalized α-shape handles lines (generalized points) as if they are infinite collections of points. The guided α-shape handles lines (guides) as indicators for preferred locations for the boundary of the shape. The guided α-shape uses (parts of) these lines as boundary where the points suggest that this is appropriate. We prove that, for an input of n points and m guides, both the generalized and guided α-shape can be constructed in O((n + m) log(n + m)) time. We apply guided α-shapes to urban reconstruction from LiDAR, where neighboring surfaces can be connected conveniently along their intersection lines into adjacent surfaces of a 3D model. We analyze guided α-shapes of both synthetic and real data and show they are consistently better than α-shapes for this application.

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Completion and Reconstruction with Primitive Shapes

Cited by 121 publications

References 38 publications

A Review Of Point Clouds Segmentation and Classification Algorithms

A Review Of Point Clouds Segmentation and Classification Algorithms

Structure recovery by part assembly

On the shape of a set of points and lines in the plane

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