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Published in:
2016 | OriginalPaper | Chapter
Energy-Based Multi-plane Detection from 3D Point Clouds
Authors : Liang Wang, Chao Shen, Fuqing Duan, Ping Guo
Published in: Neural Information Processing
Publisher: Springer International Publishing
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Abstract
Detecting multi-plane from 3D point clouds can provide concise and meaningful abstractions of 3D data and give users higher-level interaction possibilities. However, existing algorithms are deficient in accuracy and robustness, and highly dependent on thresholds. To overcome these deficiencies, a novel method is proposed, which detects multi-plane from 3D point clouds by labeling points instead of greedy searching planes. It first generates initial models. Second, it computes energy terms and constructs the energy function. Third, the point labeling problem is solved by minimizing the energy function. Then, it refines the labels and parameters of detected planes. This process is iterated until the energy does not decrease. Finally, multiple planes are detected. Experimental results validate the proposed method. It outperforms existing algorithms in accuracy and robustness. It also alleviates the high dependence on thresholds and the unknown number of planes in 3D point clouds.