PIE-NET: Parametric Inference of Point Cloud Edges
- URL: http://arxiv.org/abs/2007.04883v2
- Date: Sun, 25 Oct 2020 15:25:35 GMT
- Title: PIE-NET: Parametric Inference of Point Cloud Edges
- Authors: Xiaogang Wang, Yuelang Xu, Kai Xu, Andrea Tagliasacchi, Bin Zhou, Ali
Mahdavi-Amiri, Hao Zhang
- Abstract summary: We introduce an end-to-end learnable technique to robustly identify feature edges in 3D point cloud data.
Our deep neural network, coined PIE-NET, is trained for parametric inference of edges.
- Score: 40.27043782820615
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: 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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