A random sampling strategy for piecewise planar scene segmentation

We investigate the problem of automatically creating 3D models of man-made environments that we represent as collections of textured planes. A typical approach is to automatically reconstruct a sparse 3D model made of points, and to manually indicate their plane membership, as well as the delineation of the planes: this is the piecewise planar segmentation phase. Texture images are then extracted by merging perspectively corrected input images. We propose an automatic approach to the piecewise planar segmentation phase, that detects the number of planes to approximate the scene surface to some extent, and the parameters of these planes, from a sparse 3D model made of points. Our segmentation method is inspired from the robust estimator RANSAC. It generates and scores plane hypotheses by random sampling of the 3D points. Our plane scoring function and our plane comparison function, required to prevent detecting the same plane twice, are designed to detect planes with large or small sup...

• Type: Journal
• Year: 2007
• Where: CVIU
• Authors: Adrien Bartoli
• Keywords: 3D Model, CVIU 2007, Piecewise Planar Segmentation, Planar Segmentation Phase