Robust, Efficient and Deterministic Planes Detection in Unorganized Point Clouds Based on Sliding Voxels

Abstract

Planes detection in unorganized point clouds is a fundamental and essential task in 3D computer vision. It is a prerequisite in a wide variety of tasks such as object recognition, registration,and reconstruction. Conventional plane detection methods are remarkably slow because they require the computation of point-wise normal vectors and are non-deterministic due to their dependency on random sampling. Therefore, we propose a drastically more efficient and deterministic approach based on slidingvoxels. A sliding voxel is an overlapping grid structure in which we analyze the planarity of the points distributions to extract hypothetical planes efficiently. Each possible plane is validated globally by weighing and comparing its co-planarity with other sliding voxels’ planes. Experimental results with simulated and realistic point clouds confirmed that the proposed method is several times faster, more accurate, and more robust to noise than conventional methods.