Generating compact polygonal models from point clouds is a key problem in 3D vision and computer graphics. However, due to inherent limitations of LiDAR scanning (e.g. range constraints and occlusions), critical scene information is often missing, leading to degraded reconstruction accuracy. To address this, we propose a plane assembling strategy that effectively recovers missing details while maintaining model compactness. We classify all the planes extracted from the scene into three categories: highly visible, barely visible, and invisible. The invisible planes, which are recovered by scene structure analysis, indicate the missing details. The three types of planes correspond to the three growth priorities. Each plane grows according to the priority level, and the space is partitioned progressively, namely, the hierarchical partition. Subsequently, we generate a watertight polygonal mesh from the partition via a min-cut-based optimization. Finally, comparisons on public datasets show the effectiveness and superiority of our method against mainstream approaches. The project page is available at https://hsr-3dv.github.io/.

翻译：从点云生成紧凑多边形模型是三维视觉与计算机图形学中的关键问题。然而，由于激光雷达扫描的固有局限性（例如距离约束和遮挡），关键场景信息常常缺失，导致重建精度下降。为解决这一问题，我们提出了一种平面组装策略，在保持模型紧凑性的同时有效恢复缺失细节。我们将从场景中提取的所有平面分为三类：高可见、弱可见和不可见。通过场景结构分析恢复的不可见平面指示了缺失细节。三类平面对应三种增长优先级。每个平面按优先级水平生长，空间逐步被划分，即层次分割。随后，我们通过基于最小割的优化从分割中生成水密多边形网格。最后，在公共数据集上的比较表明，我们的方法相对于主流方法具有有效性和优越性。项目页面见https://hsr-3dv.github.io/。