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Efficient, General Point Cloud Registration with Kernel Feature Maps
2013
2013 International Conference on Computer and Robot Vision
This paper proposes a novel and efficient point cloud registration algorithm based on the kernel-induced feature map. Point clouds are mapped to a high-dimensional (Hilbert) feature space, where they are modeled with Gaussian distributions. A rigid transformation is first computed in feature space by elegantly computing and aligning a small number of eigenvectors with kernel PCA (KPCA) and is then projected back to 3D space by minimizing a consistency error. SE(3) on-manifold optimization is
doi:10.1109/crv.2013.26
dblp:conf/crv/XiongSP13
fatcat:o7v67xzrinagxasrspi3vov7gu