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

TL;DR

This paper introduces a novel multi-constraint RANSAC method for simultaneous estimation of multiple plane models from noisy 3D point clouds, leveraging scene knowledge to improve accuracy in applications like indoor mapping.

Contribution

It presents a new approach combining scene knowledge with multi-constraint RANSAC for improved multi-plane model estimation from 3D point clouds.

Findings

Outperforms state-of-the-art methods in clustering and plane estimation.

Effectively incorporates scene constraints to enhance model accuracy.

Demonstrates robustness in noisy data scenarios.

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.