Vectorized reconstruction from indoor point cloud has attracted increasing attention in recent years due to its high regularity and low memory consumption. Compared with aerial mapping of outdoor urban environments, indoor point cloud generated by LiDAR scanning or image-based 3D reconstruction usually contain more clutter and missing areas, which greatly increase the difficulty of vectorized reconstruction. In this paper, we propose an effective multistep pipeline to reconstruct vectorized models from indoor point cloud without the Manhattan or Atlanta world assumptions. The core idea behind our method is the combination of a sequence of 2D segment or cell assembly problems that are defined as global optimizations while reducing the reconstruction complexity and enhancing the robustness to different scenes. The proposed method includes a semantic segmentation stage and a reconstruction stage. First, we segment the permanent structures of indoor scenes, including ceilings, floors, walls and cylinders, from the input data, and then, we reconstruct these structures in sequence. The floorplan is first generated by detecting wall planes and selecting optimal subsets of projected wall segments with Integer Linear Programming (ILP), followed by constructing a 2D arrangement and recovering the ceiling and floor structures by Markov Random Filed (MRF) labeling on the arrangement. Finally, the wall structures are modeled by lifting each edge of the arrangement to a proper height by means of another global optimization. Merging the respective results yields the final model. The experimental results show that the proposed method could obtain accurate and compact vectorized models on both precise LiDAR data and defect-laden MVS data compared with other state-of-the-art approaches.

近年来，由于室内点云的向量化重构具有较高的规则性和较低的内存消耗，因此越来越受到关注。与室外城市环境的空中制图相比，LiDAR扫描或基于图像的3D重建生成的室内点云通常包含更多的混乱区域和缺失区域，这大大增加了矢量化重建的难度。在本文中，我们提出了一种有效的多步流水线，可以在没有曼哈顿或亚特兰大世界假设的情况下从室内点云重构矢量化模型。我们方法背后的核心思想是将一系列2D片段或单元格装配问题组合在一起，这些问题被定义为全局优化，同时降低了重建的复杂性并增强了对不同场景的鲁棒性。所提出的方法包括语义分割阶段和重构阶段。首先，我们根据输入数据分割室内场景的永久性结构，包括天花板，地板，墙壁和圆柱体，然后按顺序重建这些结构。首先通过检测墙平面并使用整数线性规划（ILP）选择投影墙段的最佳子集来生成平面图，然后构造2D布置并通过布置上的马尔可夫随机场（MRF）标记来恢复天花板和地板结构。最后，通过另一个全局优化方法，通过将布置的每个边缘提升到适当的高度来对墙壁结构进行建模。合并各个结果将生成最终模型。