Parametric Point Cloud Completion for Polygonal Surface Reconstruction
This addresses a bottleneck in 3D modeling for applications like robotics or CAD by improving reconstruction quality from incomplete data, though it is a novel method rather than a broad paradigm shift.
The paper tackles the problem of polygonal surface reconstruction from incomplete point clouds by introducing parametric completion, which recovers parametric primitives instead of individual points, resulting in superior performance on the ABC dataset.
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.