Three-dimensional planar model estimation using multi-constraint knowledge based on k-means and RANSAC
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
Cite
@article{arxiv.1708.01143,
title = {Three-dimensional planar model estimation using multi-constraint knowledge based on k-means and RANSAC},
author = {Marcelo Saval-Calvo and Jorge Azorin-Lopez and Andres Fuster-Guillo and Jose Garcia-Rodriguez},
journal= {arXiv preprint arXiv:1708.01143},
year = {2017}
}