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

arXiv: https://arxiv.org/abs/1708.01143 · HTML: https://mlsanity.com/paper/1708.01143/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 3 Aug 2017.

## Built on by (most cited first, 1 of 1)

- [Marker-Based Extrinsic Calibration Method for Accurate Multi-Camera 3D Reconstruction](https://mlsanity.com/paper/2505.02539/) (2025)

## Abstract (arXiv)

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.