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FRANSAC: Fast RANdom Sample Consensus for 3D Plane Segmentation

International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Year of Publication: 2017
Ramy Ashraf Zeineldin, Nawal Ahmed El-Fishawy

Ramy Ashraf Zeineldin and Nawal Ahmed El-Fishawy. FRANSAC: Fast RANdom Sample Consensus for 3D Plane Segmentation. International Journal of Computer Applications 167(13):30-36, June 2017. BibTeX

	author = {Ramy Ashraf Zeineldin and Nawal Ahmed El-Fishawy},
	title = {FRANSAC: Fast RANdom Sample Consensus for 3D Plane Segmentation},
	journal = {International Journal of Computer Applications},
	issue_date = {June 2017},
	volume = {167},
	number = {13},
	month = {Jun},
	year = {2017},
	issn = {0975-8887},
	pages = {30-36},
	numpages = {7},
	url = {},
	doi = {10.5120/ijca2017914558},
	publisher = {Foundation of Computer Science (FCS), NY, USA},
	address = {New York, USA}


Scene analysis is a prior stage in many computer vision and robotics applications. Thanks to recent depth camera, we propose a fast plane segmentation approach for obstacle detection in indoor environments. The proposed method Fast RANdom Sample Consensus (FRANSAC) involves three steps: data input, data preprocessing and 3D RANSAC. Firstly, range data, obtained from 3D camera, is converted into 3D point clouds. Next, a preprocessing stage is introduced where a pass through and voxel grid filters are applied. Finally, planes are estimated using a modified 3D RANSAC. The experimental results demonstrate that our approach can segment planes and detect obstacles about 7 times faster than the standard RANSAC without losing the discriminative power.


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RANSAC, point cloud, plane segmentation, Kinect, RGB-D, Voxel