Plane segmentation from point clouds using the detail preserving optimal-vector-field

Abstract

Plane segmentation in three dimensions is a crucial step for many applications. A recent optimal-vector-field (OVF) technique demonstrated good generality across a variety of models. However, OVF is a rough approach that results in under-segmentation and missing points due to loss of details. Hence, this paper presents a new plane segmentation method that uses the detail-preserving OVF method to address these problems. There are three improvements to our proposed segmentation method. (1) To enlarge the vector difference between points on different planes, we split the model into a set of planar primitives leveraging the fine planar primitives extraction method, and then estimate the normal of each point in the primitive as the vector field. (2) We define a point-based Laplace operator to improve the vector field optimization process, thereby enhancing the accuracy of OVF for detail detection. (3) We innovatively take the magnitude of optimal-vector-field as the criterion for planar primitive-based growth to obtain the final segmentation result. The evaluation of four datasets shows that our method achieves higher average precision and recall than the OVF method by 16.43% and 20.79% respectively, and the global consistency error (GCE) decreases by 6.62%. The evaluation indicates that our method is capable of preserving finer details.