Fast Regularity-Constrained Plane Reconstruction
This work addresses robust plane reconstruction for computer vision applications in structured environments, representing an incremental improvement over existing regularity-constrained methods.
The paper tackles plane reconstruction in man-made environments by leveraging geometric relationships like parallelism and orthogonality with a constraint model requiring minimal prior knowledge, resulting in an algorithm that outperforms state-of-the-art methods in speed and robustness under high noise and outlier conditions.
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 reconstruction 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 reconstructs planes under high percentages of noise and outliers. This is superior to other state-of-the-art regularity-constrained plane reconstruction methods in terms of speed and robustness.