Separability Membrane: 3D Active Contour for Point Cloud Surface Reconstruction
This addresses surface reconstruction from point clouds for applications like 3D modeling, but it is incremental as it builds on active contour methods with a new separability criterion.
The paper tackles the problem of extracting surfaces from 3D point clouds by proposing Separability Membrane, a method that defines surfaces as boundaries maximizing feature separability using Fisher's ratio, and it demonstrates effectiveness and robustness on synthetic and 3DNet datasets.
This paper proposes Separability Membrane, a robust 3D active contour for extracting a surface from 3D point cloud object. Our approach defines the surface of a 3D object as the boundary that maximizes the separability of point features, such as intensity, color, or local density, between its inner and outer regions based on Fisher's ratio. Separability Membrane identifies the exact surface of a 3D object by maximizing class separability while controlling the rigidity of the 3D surface model with an adaptive B-spline surface that adjusts its properties based on the local and global separability. A key advantage of our method is its ability to accurately reconstruct surface boundaries even when they are ambiguous due to noise or outliers, without requiring any training data or conversion to volumetric representation. Evaluations on a synthetic 3D point cloud dataset and the 3DNet dataset demonstrate the membrane's effectiveness and robustness under diverse conditions.