Mesh filtering algorithm using an adaptive 3D convolution kernel applied to a volume-based vector distance field

This paper addresses the problem of feature preserving mesh filtering, which occurs in surface reconstruction of scanned objects, which include acquisition noise to be removed without altering sharp edges. We propose a method based on a vector field distance transform of the mesh to process. It is a volume-based implicit surface modeling, which provides an alternative representation of meshes. We use an adaptive 3D convolution kernel applied to the voxels of the distance transform model. Weights of the kernel elements are determined according to the angle between the vectors of the implicit field. We also propose a new adaptation of the Marching Cubes algorithm in order to extract the isosurface from the vector implicit field after the filtering process. We compare our method to the previous one introduced using the vector field representation and to other feature preserving adaptive filtering algorithms. According to error metric evaluations, we show that our new design provides high quality filtering results while better preserving geometric features.

Graphical abstractVFDT filtering comparison using the same triangulation algorithm: (a) reference bunny, (b) noisy model, (c) previous VFDT filtering algorithm with a noise variance=3.8×10−3, (d) color map of the error metric evaluation for the previous filtering algorithm, (e) our new VFDT filtering algorithm with threshold angle τ=0.75 and (f) color map of the error metric evaluation for our new filtering algorithm. A Gaussian kernel type of 3×3×3 voxels was used for both filtering algorithms.Figure optionsDownload full-size imageDownload high-quality image (159 K)Download as PowerPoint slideHighlights► We propose a new mesh filtering algorithm to remove noise from scanned objects. ► This algorithm works on the vector field distance transform (VFDT) of the mesh. ► We use a 3D adaptive kernel based on angle threshold between the vectors of the VFDT. ► This kernel is used to perform the 3D convolution filtering of the VFDT. ► We also introduce a new adaptation of the Marching Cubes to triangulate the VFDT.