PIE-NET: Parametric Inference of Point Cloud Edges
This work addresses the challenge of edge detection in 3D point clouds for applications like CAD modeling, though it appears incremental as it builds on existing region proposal architectures.
The paper tackled the problem of robustly identifying feature edges in 3D point cloud data by introducing PIE-NET, an end-to-end learnable technique that represents edges as parametric curves, resulting in significant quantitative and qualitative improvements over state-of-the-art methods on the ABC dataset.
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.