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Scale alignment of 3D point clouds with different scales

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Abstract

In this paper, we propose two methods for estimating the scales of point clouds to align them. The first method estimates the scale of each point cloud separately: each point cloud has its own scale that is something like the size of a scene. We call it a keyscale, which is a representative scale and is defined for a given 3D point cloud as the minimum of the cumulative contribution rates of PCA of descriptors over different scales. Our second method directly estimates the ratio of scales (scale ratio) of two point clouds. Instead of finding the minimum, this approach registers the two sets of curves of the cumulative contribution rate of PCA by assuming that those differ only in scale. Experimental results with simulated and real scene point clouds demonstrate that the scale alignment of 3D point clouds can be effectively accomplished by our scale ratio estimation.

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  1. http://www.youtube.com/watch?v=ZNIkZ5Dd0EU.

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Acknowledgments

This work was supported in part by JSPS KAKENHI Grant Number 23700211.

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Correspondence to Baowei Lin.

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This paper extends the conference version [17, 26] with additional experimental results and extra detailed discussions.

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Lin, B., Tamaki, T., Zhao, F. et al. Scale alignment of 3D point clouds with different scales. Machine Vision and Applications 25, 1989–2002 (2014). https://doi.org/10.1007/s00138-014-0633-2

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