Ms. Lu Cao Profile

Lu Cao

at Leiden Univ

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SPIE Journal Paper | 21 October 2013

Analytical evaluation of algorithms for point cloud surface reconstruction using shape features

Lu Cao, Fons Verbeek

JEI, Vol. 22, Issue 04, 043008, (October 2013) https://doi.org/10.1117/12.10.1117/1.JEI.22.4.043008

KEYWORDS: Error analysis, Reconstruction algorithms, Optical spheres, Clouds, 3D modeling, Shape analysis, Biological research, Algorithm development, 3D image processing, Statistical modeling

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In computer vision and graphics, reconstruction of a three-dimensional surface from a point cloud is a well-studied research area. As the surface contains information that can be measured, the application of surface reconstruction may be potentially important for applications in bioimaging. In the past decade, a number of algorithms for surface reconstruction have been developed. Generally speaking, these algorithms can be separated into two categories: explicit representation and implicit approximation. Most of these algorithms have a sound basis in mathematical theory. However, so far, no analytical evaluation between these algorithms has been presented. The straightforward method of evaluation has been by convincing through visual inspection. Therefore, we design an analytical approach by selecting surface distance, surface area, and surface curvature as three major surface descriptors. We evaluate these features in varied conditions. Our ground truth values are obtained from analytical shapes: the sphere, the ellipsoid, and the oval. Through evaluation we search for a method that can preserve the surface characteristics best and which is robust in the presence of noise. The results obtained from our experiments indicate that Poisson reconstruction method performs best. This outcome can now be used to produce reliable surface reconstruction of biological models.

Proceedings Article | 30 January 2012 Paper

Evaluation of algorithms for point cloud surface reconstruction through the analysis of shape parameters

Lu Cao, Fons Verbeek

Proceedings Volume 8290, 82900G (2012) https://doi.org/10.1117/12.906718

KEYWORDS: Reconstruction algorithms, Error analysis, Optical spheres, Clouds, 3D modeling, Shape analysis, Biological research, 3D image processing, Analytical research, Visualization

Read Abstract +

In computer graphics and visualization, reconstruction of a 3D surface from a point cloud is an important research area. As the surface contains information that can be measured, i.e. expressed in features, the application of surface reconstruction can be potentially important for application in bio-imaging. Opportunities in this application area are the motivation for this study. In the past decade, a number of algorithms for surface reconstruction have been proposed. Generally speaking, these methods can be separated into two categories: i.e., explicit representation and implicit approximation. Most of the aforementioned methods are firmly based in theory; however, so far, no analytical evaluation between these methods has been presented. The straightforward way of evaluation has been by convincing through visual inspection. Through evaluation we search for a method that can precisely preserve the surface characteristics and that is robust in the presence of noise. The outcome will be used to improve reliability in surface reconstruction of biological models. We, therefore, use an analytical approach by selecting features as surface descriptors and measure these features in varying conditions. We selected surface distance, surface area and surface curvature as three major features to compare quality of the surface created by the different algorithms. Our starting point has been ground truth values obtained from analytical shapes such as the sphere and the ellipsoid. In this paper we present four classical surface reconstruction methods from the two categories mentioned above, i.e. the Power Crust, the Robust Cocone, the Fourier-based method and the Poisson reconstruction method. The results obtained from our experiments indicate that Poisson reconstruction method performs the best in the presence of noise.

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