Spectral surface reconstruction from noisy point clouds

Kolluri, Ravikrishna; Shewchuk, Jonathan Richard; O’Brien, James F.

doi:10.1145/1057432.1057434

Cited by 198 publications

(130 citation statements)

References 27 publications

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“…Kolluri et al [26] present a spectral surface reconstruction method. This algorithm, first, creates a Delaunay tetrahedralization from the point set and then starts to prune the surface by spectral graph partitioning.…”

Section: Explicit Methodsmentioning

confidence: 99%

“…Kolluri et al [26] explain that their proposed method "can ignore outliers, patch holes and undersampled regions, and surmount ambiguity due to measurement errors" [26]. The reconstructed surfaces of this method are provided in [26].…”

Section: Triangulated Explicit Methodsmentioning

confidence: 99%

“…The reconstructed surfaces of this method are provided in [26]. The surface reconstruction time for this algorithm is high and it cannot generate sharp edges.…”

Section: Triangulated Explicit Methodsmentioning

confidence: 99%

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Survey on 3D Surface Reconstruction

2016

J Inf Process Syst

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The recent advent of increasingly affordable and powerful 3D scanning devices capable of capturing high resolution range data about real-world objects and environments has fueled research into effective 3D surface reconstruction techniques for rendering the raw point cloud data produced by many of these devices into a form that would make it usable in a variety of application domains. This paper, therefore, provides an overview of the existing literature on surface reconstruction from 3D point clouds. It explains some of the basic surface reconstruction concepts, describes the various factors used to evaluate surface reconstruction methods, highlights some commonly encountered issues in dealing with the raw 3D point cloud data and delineates the tradeoffs between data resolution/accuracy and processing speed. It also categorizes the various techniques for this task and briefly analyzes their empirical evaluation results demarcating their advantages and disadvantages. The paper concludes with a cross-comparison of methods which have been evaluated on the same benchmark data sets along with a discussion of the overall trends reported in the literature. The objective is to provide an overview of the state of the art on surface reconstruction from point cloud data in order to facilitate and inspire further research in this area.

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Section: Explicit Methodsmentioning

confidence: 99%

Section: Triangulated Explicit Methodsmentioning

confidence: 99%

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Survey on 3D Surface Reconstruction

2016

J Inf Process Syst

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“…Surface reconstruction from unorganized point clouds has been intensively studied; see [3], [12], [13], [16], [27], [40] and references therein. Despite the increased availability of commodity parallel platforms, there has been very little work on parallel algorithms for surface reconstruction.…”

Section: Surface Reconstructionmentioning

confidence: 99%

Fast Sparse Level Sets on Graphics Hardware

Jalba

Laan

Roerdink

2013

IEEE Trans. Visual. Comput. Graphics

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Abstract-The level set method is one of the most popular techniques for capturing and tracking deformable interfaces. Although level sets have demonstrated great potential in visualization and computer graphics applications, such as surface editing and physically-based modeling, their use for interactive simulations has been limited due to the high computational demands involved. In this paper we address this computational challenge by leveraging the increased computing power of graphics processors, to achieve fast simulations based on level sets. Our efficient, sparse GPU level set method is substantially faster than other state-of-the-art, parallel approaches on both CPU and GPU hardware. We further investigate its performance through a method for surface reconstruction, based on GPU level sets. Our novel multi-resolution method for surface reconstruction from unorganized point clouds compares favorably with recent, existing techniques and other parallel implementations. Finally, we point out that both level set computations and rendering of level-set surfaces can be performed at interactive rates, even on large volumetric grids. Therefore, many applications based on level sets can benefit from our sparse level set method.

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“…Most existing algorithms focus on optimal [11] and noisy sampling types [30,44,31] that require input data to be of good sampling density that satisfies the ε-sampling criterion (described in Section 2.2.2). However, very few works have addressed the issue on how to handle under-sampled point sets, or rather, to handle regions of under-sampling in a point set.…”

Section: The Surface Reconstruction Problemmentioning

confidence: 99%