Abstract
The computation of the minimum-volume bounding box of a point set in ℝ3 is a hard problem. The best known exact algorithm requires O(n 3) time, so several approximation algorithms and heuristics are preferred in practice. Among them, the algorithm based on PCA (Principal Component Analysis) plays an important role. Recently, it has been shown that the discrete PCA algorithm may fail to approximate the minimum-volume bounding box even for a large constant factor. Moreover, this happens only for some very special examples with point clusters. As an alternative, it has been proved that the continuous version of PCA overcomes these problems.
The contribution of this paper is two-fold. First, we study the impact of the recent theoretical results on applications of several PCA variants in practice. We analyze the advantages and disadvantages of the different variants on realistic inputs, randomly generated inputs, and specially constructed (worst case) instances. Second, we evaluate and compare the performances of several existing bounding box algorithms.
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Dimitrov, D., Holst, M., Knauer, C., Kriegel, K. (2009). Closed-Form Solutions for Continuous PCA and Bounding Box Algorithms. In: Ranchordas, A., Araújo, H.J., Pereira, J.M., Braz, J. (eds) Computer Vision and Computer Graphics. Theory and Applications. VISIGRAPP 2008. Communications in Computer and Information Science, vol 24. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-10226-4_3
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DOI: https://doi.org/10.1007/978-3-642-10226-4_3
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