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PIE-NET: Parametric Inference of Point Cloud Edges

Xiaogang Wang · Yuelang Xu · Kai Xu · Andrea Tagliasacchi · Bin Zhou · Ali Mahdavi-Amiri · Hao Zhang

Poster Session 1 #476

Keywords: [ Theory ] [ Game Theory and Computational Economics ] [ Fairness, Accountability, and Transparency ] [ Applications ]


We introduce an end-to-end learnable technique to robustly identify feature edges in 3D point cloud data. We represent these edges as a collection of parametric curves (i.e.,~lines, circles, and B-splines). Accordingly, our deep neural network, coined PIE-NET, is trained for parametric inference of edges. The network relies on a "region proposal" architecture, where a first module proposes an over-complete collection of edge and corner points, and a second module ranks each proposal to decide whether it should be considered. We train and evaluate our method on the ABC dataset, a large dataset of CAD models, and compare our results to those produced by traditional (non-learning) processing pipelines, as well as a recent deep learning based edge detector (EC-NET). Our results significantly improve over the state-of-the-art from both a quantitative and qualitative standpoint.

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