Local Hough Transform for 3D Primitive Detection

Detecting primitive geometric shapes, such as cylinders, planes and spheres, in 3D point clouds is an important building block for many high-level vision tasks. One approach for this detection is the Hough Transform, where features vote for parameters that explain them. However, as the voting space grows exponentially with the number of parameters, a full voting scheme quickly becomes impractical. Solutions in the literature, such as decomposing the global voting space, often degrade the robustness w.r.t. Noise, clutter or multiple primitive instances. We instead propose a local Hough Transform, which votes on sub-manifolds of the original parameter space. For this, only those parameters are considered that align a given scene point with the primitive. The voting then recovers the locally best fitting primitive. This local detection scheme is embedded in a coarse-to-fine detection pipeline, which refines the found candidates and removes duplicates. The evaluation shows high robustness against clutter and noise, as well as competitive results w.r.t. Prior art.