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arXiv · 1708.01143

Three-dimensional planar model estimation using multi-constraint knowledge based on k-means and RANSAC

Marcelo Saval-Calvo·Jorge Azorin-Lopez·Andres Fuster-Guillo·Jose Garcia-Rodriguez
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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.

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Marcelo Saval-Calvo, Jorge Azorin-Lopez, Andres Fuster-Guillo, Jose Garcia-Rodriguez. 2017-08-03. Three-dimensional planar model estimation using multi-constraint knowledge based on k-means and RANSAC. https://doi.org/10.1016/j.asoc.2015.05.007

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