Abstract
The majority of the existing techniques for surface reconstruction and the closely related problem of normal estimation are deterministic. Their main advantages are the speed and, given a reasonably good initial input, the high quality of the reconstructed surfaces. Nevertheless, their deterministic nature may hinder them from effectively handling incomplete data with noise and outliers. In our previous work [1], we applied a statistical technique, called ensembles, to the problem of surface reconstruction. We showed that an ensemble can improve the performance of a deterministic algorithm by putting it into a statistics based probabilistic setting. In this paper, with several experiments, we further study the suitability of ensembles in surface reconstruction, and also apply ensembles to normal estimation. We experimented with a widely used normal estimation technique [2] and Multi-level Partitions of Unity implicits for surface reconstruction [3], showing that normal and surface ensembles can successfully be combined to handle noisy point sets.
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Yoon, M., Lee, Y., Lee, S., Ivrissimtzis, I., Seidel, HP. (2006). Ensembles for Normal and Surface Reconstructions. In: Kim, MS., Shimada, K. (eds) Geometric Modeling and Processing - GMP 2006. GMP 2006. Lecture Notes in Computer Science, vol 4077. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11802914_2
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DOI: https://doi.org/10.1007/11802914_2
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