Abstract
In this paper we propose a new descriptor for 3D point clouds that is fast when compared to others with similar performance and its parameters are set using a genetic algorithm. The idea is to obtain a descriptor that can be used in simple computational devices, that have no GPUs or high computational capabilities and also avoid the usual time-consuming task of determining the optimal parameters for the descriptor. Our proposal is compared with other similar algorithms in a public available point cloud library (PCL [1]). We perform a comparative evaluation on 3D point clouds using both the object and category recognition performance. Our proposal presents a comparable performance with other similar algorithms but is much faster and requires less disk space.
We acknowledge the financial support of project PEst-OE/EEI/LA0008/2013.
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Węgrzyn, D., Alexandre, L.A. (2013). A Genetic Algorithm-Evolved 3D Point Cloud Descriptor. In: Ruiz-Shulcloper, J., Sanniti di Baja, G. (eds) Progress in Pattern Recognition, Image Analysis, Computer Vision, and Applications. CIARP 2013. Lecture Notes in Computer Science, vol 8258. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-41822-8_12
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DOI: https://doi.org/10.1007/978-3-642-41822-8_12
Publisher Name: Springer, Berlin, Heidelberg
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