用分层空间划分恢复点云缺失细节，生成紧凑闭合网格。
Hierarchical Space Partition for Surface Reconstruction
- 按可见性分三类平面，分优先级逐层生长划分空间
- 在ScanNet和ShapeNet上重建精度优于主流方法
- 适合处理激光扫描中因遮挡导致的细节缺失问题
从点云生成紧凑多边形模型是3D视觉与计算机图形学中的关键问题。由于激光雷达扫描固有的局限性（如距离限制和遮挡），场景关键信息常缺失，导致重建精度下降。为此，我们提出一种平面组装策略，能有效恢复缺失细节并保持模型紧凑。我们将场景中提取的所有平面分为三类：高可见、低可见和不可见。其中，通过场景结构分析恢复的不可见平面代表缺失细节。这三类平面对应三种生长优先级，各平面按优先级逐步生长，实现空间的分层划分。随后，基于最小割优化生成闭合多边形网格。在公开数据集上的对比实验表明，该方法在效果和性能上均优于主流方法。
原文摘要 · Abstract (English)
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/.
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