# BoundED: Neural Boundary and Edge Detection in 3D Point Clouds via Local Neighborhood Statistics

arXiv: https://arxiv.org/abs/2210.13305 · HTML: https://mlsanity.com/paper/2210.13305/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Builds on 24 of its 84 references that are in the map.
- First posted on arXiv 24 Oct 2022.

## Builds on (most cited first, 24 of 24)

- [Adam: A Method for Stochastic Optimization](https://mlsanity.com/paper/1412.6980/) (2014)
- [PyTorch: An Imperative Style, High-Performance Deep Learning Library](https://mlsanity.com/paper/1912.01703/) (2019)
- [PointNet: Deep Learning on Point Sets for 3D Classification and Segmentation](https://mlsanity.com/paper/1612.00593/) (2016)
- [PointNet++: Deep Hierarchical Feature Learning on Point Sets in a Metric Space](https://mlsanity.com/paper/1706.02413/) (2017)
- [KPConv: Flexible and Deformable Convolution for Point Clouds](https://mlsanity.com/paper/1904.08889/) (2019)
- [Accelerating 3D Deep Learning with PyTorch3D](https://mlsanity.com/paper/2007.08501/) (2020)
- [Deep Learning for 3D Point Clouds: A Survey](https://mlsanity.com/paper/1912.12033/) (2019)
- [Large-scale Point Cloud Semantic Segmentation with Superpoint Graphs](https://mlsanity.com/paper/1711.09869/) (2017)
- [ABC: A Big CAD Model Dataset For Geometric Deep Learning](https://mlsanity.com/paper/1812.06216/) (2018)
- [SEGCloud: Semantic Segmentation of 3D Point Clouds](https://mlsanity.com/paper/1710.07563/) (2017)
- [Semantic3D.net: A new Large-scale Point Cloud Classification Benchmark](https://mlsanity.com/paper/1704.03847/) (2017)
- [PointCleanNet: Learning to Denoise and Remove Outliers from Dense Point Clouds](https://mlsanity.com/paper/1901.01060/) (2019)
- [PCPNET: Learning Local Shape Properties from Raw Point Clouds](https://mlsanity.com/paper/1710.04954/) (2017)
- [EC-Net: an Edge-aware Point set Consolidation Network](https://mlsanity.com/paper/1807.06010/) (2018)
- [Deep Projective 3D Semantic Segmentation](https://mlsanity.com/paper/1705.03428/) (2017)
- [PIE-NET: Parametric Inference of Point Cloud Edges](https://mlsanity.com/paper/2007.04883/) (2020)
- [JSENet: Joint Semantic Segmentation and Edge Detection Network for 3D Point Clouds](https://mlsanity.com/paper/2007.06888/) (2020)
- [Linking Points With Labels in 3D: A Review of Point Cloud Semantic Segmentation](https://mlsanity.com/paper/1908.08854/) (2019)
- [Beyond single receptive field: A receptive field fusion-and-stratification network for airborne laser scanning point cloud classification](https://mlsanity.com/paper/2207.10278/) (2022)
- [PCEDNet : A Lightweight Neural Network for Fast and Interactive Edge Detection in 3D Point Clouds](https://mlsanity.com/paper/2011.01630/) (2020)
- [Edge and Corner Detection for Unorganized 3D Point Clouds with Application to Robotic Welding](https://mlsanity.com/paper/1809.10468/) (2018)
- [Fast 3D Line Segment Detection From Unorganized Point Cloud](https://mlsanity.com/paper/1901.02532/) (2019)
- [Learning Part Boundaries from 3D Point Clouds](https://mlsanity.com/paper/2007.07563/) (2020)
- [DEF: Deep Estimation of Sharp Geometric Features in 3D Shapes](https://mlsanity.com/paper/2011.15081/) (2020)

## Built on by (most cited first, 3 of 3)

- [STAR-Edge: Structure-aware Local Spherical Curve Representation for Thin-walled Edge Extraction from Unstructured Point Clouds](https://mlsanity.com/paper/2503.00801/) (2025)
- [EdgeFormer: local patch-based edge detection transformer on point clouds](https://mlsanity.com/paper/2604.21387/) (2026)
- [One Shot Learning for Edge Detection on Point Clouds](https://mlsanity.com/paper/2604.22354/) (2026)

## Abstract (arXiv)

Extracting high-level structural information from 3D point clouds is challenging but essential for tasks like urban planning or autonomous driving requiring an advanced understanding of the scene at hand. Existing approaches are still not able to produce high-quality results consistently while being fast enough to be deployed in scenarios requiring interactivity. We propose to utilize a novel set of features describing the local neighborhood on a per-point basis via first and second order statistics as input for a simple and compact classification network to distinguish between non-edge, sharp-edge, and boundary points in the given data. Leveraging this feature embedding enables our algorithm to outperform the state-of-the-art techniques in terms of quality and processing time.