Parametric Point Cloud Completion for Polygonal Surface Reconstruction

Zhaiyu Chen1,3, Yuqing Wang1, Liangliang Nan2, Xiao Xiang Zhu1,3,
1Technical University of Munich, 2Delft University of Technology, 3Munich Center for Machine Learning
CVPR 2025
Paper arXiv Video Code
PaCo teaser figure

PaCo recovers parametric primitives beyond individual points for polygonal surface reconstruction.

Overview

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.

Comparison with other methods

Input (-25%)
GT
PaCo
ODGNet
AdaPoinTr
PoinTr
Input (-50%)
GT
PaCo
ODGNet
AdaPoinTr
PoinTr
Input (-75%)
GT
PaCo
ODGNet
AdaPoinTr
PoinTr

How it works

PaCo architecture

Architecture of PaCo. Starting from an incomplete point cloud, we hierarchically encode the points into plane proxies with a lookup table from segmentation. These proxies inform the proxy generator in producing a set of proxy proposals. The generated proxies are then passed to the parameter estimator and the point distributor for primitive estimation. Finally, the primitive selector identifies a subset of primitives to form the parametric completion, with selection optimized via bipartite matching. The resulting primitives enable direct assembly for polygonal surface reconstruction.

BibTeX

@inproceedings{chen2025paco,
  title     = {Parametric Point Cloud Completion for Polygonal Surface Reconstruction},
  author    = {Zhaiyu Chen and Yuqing Wang and Liangliang Nan and Xiao Xiang Zhu},
  booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
  year      = {2025},
}