Saval-Calvo, Marcelo, Azorin-Lopez, Jorge, Fuster-Guilló, Andrés, Garcia-Rodriguez, Jose
Three-dimensional planar model estimation using multi-constraint knowledge based on k-means and RANSAC
Applied Soft Computing. 2015, 34: 572-586. doi:10.1016/j.asoc.2015.05.007
URI: http://hdl.handle.net/10045/48187
DOI: 10.1016/j.asoc.2015.05.007
ISSN: 1568-4946 (Print)
Abstract: 
Plane model extraction from three-dimensional point clouds is a necessary step in many different applications such as planar object reconstruction, indoor mapping and indoor localization. Different RANdom SAmple Consensus (RANSAC)-based methods have been proposed for this purpose in recent years. In this study, we propose a novel method-based on RANSAC called Multiplane Model Estimation, which can estimate multiple plane models simultaneously from a noisy point cloud using the knowledge extracted from a scene (or an object) in order to reconstruct it accurately. This method comprises two steps: first, it clusters the data into planar faces that preserve some constraints defined by knowledge related to the object (e.g., the angles between faces); and second, the models of the planes are estimated based on these data using a novel multi-constraint RANSAC. We performed experiments in the clustering and RANSAC stages, which showed that the proposed method performed better than state-of-the-art methods.
Keywords:Computer vision, Model extraction, RANSAC multi-plane, Three-dimensional planes
Elsevier
info:eu-repo/semantics/article