Hough Parameter Space Regularisation for Line Detection in 3D
Manuel Jeltsch, Christoph Dalitz, Regina Pohle-Fröhlich · 2016
The Hough transform is a well known technique for detecting lines or other parametric shapes in point clouds. When it is used for nding lines in a 3D-space, an appropriate line representation and quantisation of the parameter space is necessary. In this paper, we address the problem that a straightforward quantisation of the optimal four-parameter representation of a line after Roberts results in an inhomogeneous tessellation of the geometric space that introduces bias with respect to certain line orientations. We present a discretisation of the line directions via tessellation of an icosahedron that overcomes this problem whenever one parameter in the Hough space represents a direction in 3D (e.g. for lines or planes). The new method is applied to the detection of ridges and straight edges in laser scan data of buildings, where it performs better than a straightforward quantisation.