1P.hd student , EE dep , Sharif university of technology
2student Sharif university of technology
3Phd student . Sharif university of technology
4Sharif University of Technology
Noise is inherent in digital systems. To smooth a point cloud while preserving sharp edges, a new 3D bilateral filter is introduced. It uses the point cloud normal vector in addition to color as a position vector for preserving sharp edges while smoothing the color. The bilateral filter is an averaging filter that blurs data while preserving strong edges. In 2D images, the concept of edge is defined by an abrupt change in color. In this paper, the 3D edge is defined as a change in color or point cloud normal vector. The 3D difference of Gaussian (3D DOG) is presented by subtracting two bilateral filters with different standard deviations in color and normal space. The new 3D normal-non-local means (NNM) filter is proposed for denoising the unstructured point cloud, while smoothing it with global rather than local constraint. In the proposed NNM filter, in the kd-tree nearest neighbor search in position vector, the normal vector is considered rather than colors of points. Since it needs high dimensional Gaussian filtering, the time complexity is high. Therefore, the Permutohedral lattice is used for Gaussian processing; since for high dimensional filtering of n values in d dimensions it has a time complexity of O(d^2 n) and space complexity of (dn) .
3D bilateral filtering; 3D non-local means; point cloud; Permutohedral lattice; normal of point cloud; 3D difference of Gaussian.