Robust Vectorized Surface Reconstruction with 2D-3D Joint Optimization

In this work, we propose a robust pipeline to create vectorized models from LiDAR point clouds without the assumption of watertight polygonal surfaces. The core idea behind our method is combining the hierarchical 2D projection with the pairwise intersections of clipped planes to precisely hypothesize faces of an object inside and outside while enhancing the robustness of different scenes. Moreover, to generate concise polygon meshes efficiently, Adaptive K-means++ (AKM++) is used to smooth the 2D rasterized height map followed by the Levenberg-Marquardt (LM) algorithm to optimize the projection primitive with planar intersections jointly. The final model could be easily obtained by merging the derived neighbouring facets above. Our experimental results show that the proposed method obtains compact models with high fidelity and efficiency on either precise or defect-laden data compared with other state-of-the-art approaches.