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Fast regularity-constrained plane fitting
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DOI：10.1016/j.isprsjprs.2020.01.009.png)
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Abstract
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Man-made environments typically comprise planar structures that exhibit numerous geometric relationships, such as parallelism, coplanarity, and orthogonality. Making full use of these relationships can considerably improve the robustness of algorithmic plane fitting of complex scenes. This research leverages a constraint model requiring minimal prior knowledge to implicitly establish relationships among planes. We introduce a method based on energy minimization to reconstruct the planes consistent with our constraint model. The proposed algorithm is efficient, easily to understand, and simple to implement. The experimental results show that our algorithm successfully fits planes under high percentages of noise and outliers. This is superior to other state-of-the-art regularity-constrained plane fitting methods in terms of speed and robustness.
Keywords:
Plane fitting
Point Clouds
Geometric constraint model
Regularization
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