用参数化结构补全点云，提升不完整数据下的多边形重建质量
Parametric Point Cloud Completion for Polygonal Surface Reconstruction
- 以平面参数和内点为代理，直接恢复几何结构而非单个点
- 在ABC数据集上实现更优重建效果，尤其在数据缺失严重时表现突出
- 适合需要高质量多边形建模的3D扫描与逆向工程场景
现有多边形表面重建方法高度依赖输入点云的完整性，难以处理不完整点云。我们指出，尽管当前点云补全技术能恢复缺失点，但未针对多边形表面重建优化，且忽视了底层表面的参数化表示。为此，我们提出参数化补全新范式——通过恢复参数化几何原型（如平面）来传递高层几何结构。所提出的PaCo方法利用平面代理（包含平面参数与内点）实现高质量多边形表面重建，在极端不完整数据下仍表现优异。在ABC数据集上的全面评估验证了其有效性，显著优于现有方法，为不完整数据下的多边形表面重建树立了新标准。
原文摘要 · Abstract (English)
Existing polygonal surface reconstruction methods heavily depend on input completeness and struggle with incomplete point clouds. We argue that while current point cloud completion techniques may recover missing points, they are not optimized for polygonal surface reconstruction, where the parametric representation of underlying surfaces remains overlooked. To address this gap, we introduce parametric completion, a novel paradigm for point cloud completion, which recovers parametric primitives instead of individual points to convey high-level geometric structures. Our presented approach, PaCo, enables high-quality polygonal surface reconstruction by leveraging plane proxies that encapsulate both plane parameters and inlier points, proving particularly effective in challenging scenarios with highly incomplete data. Comprehensive evaluations of our approach on the ABC dataset establish its effectiveness with superior performance and set a new standard for polygonal surface reconstruction from incomplete data. Project page: https://parametric-completion.github.io.
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