  In this paper, we propose a new algorithm for point cloud denoising based on
the tensor Tucker decomposition. We first represent the local surface patches
of a noisy point cloud to be matrices by their distances to a reference point,
and stack the similar patch matrices to be a 3rd order tensor. Then we use the
Tucker decomposition to compress this patch tensor to be a core tensor of
smaller size. We consider this core tensor as the frequency domain and remove
the noise by manipulating the hard thresholding. Finally, all the fibers of the
denoised patch tensor are placed back, and the average is taken if there are
more than one estimators overlapped. The experimental evaluation shows that the
proposed algorithm outperforms the state-of-the-art graph Laplacian regularized
(GLR) algorithm when the Gaussian noise is high ($\sigma=0.1$), and the GLR
algorithm is better in lower noise cases ($\sigma=0.04, 0.05, 0.08$).
