# Learning Part Boundaries from 3D Point Clouds

arXiv: https://arxiv.org/abs/2007.07563 · HTML: https://mlsanity.com/paper/2007.07563/

## Key facts

- Cited by 9 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 15 Jul 2020.

## Built on by (most cited first, 9 of 9)

- [Point2CAD: Reverse Engineering CAD Models from 3D Point Clouds](https://mlsanity.com/paper/2312.04962/) (2023)
- [Task-Aware Sampling Layer for Point-Wise Analysis](https://mlsanity.com/paper/2107.04291/) (2021)
- [PIG-Net: Inception based Deep Learning Architecture for 3D Point Cloud Segmentation](https://mlsanity.com/paper/2101.11987/) (2021)
- [BoundED: Neural Boundary and Edge Detection in 3D Point Clouds via Local Neighborhood Statistics](https://mlsanity.com/paper/2210.13305/) (2022)
- [Reassembling Broken Objects using Breaking Curves](https://mlsanity.com/paper/2306.02782/) (2023)
- [Deep Algebraic Fitting for Multiple Circle Primitives Extraction from Raw Point Clouds](https://mlsanity.com/paper/2204.00920/) (2022)
- [Random Walk on Point Clouds for Feature Detection](https://mlsanity.com/paper/2604.20474/) (2026)
- [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)

We present a method that detects boundaries of parts in 3D shapes represented as point clouds. Our method is based on a graph convolutional network architecture that outputs a probability for a point to lie in an area that separates two or more parts in a 3D shape. Our boundary detector is quite generic: it can be trained to localize boundaries of semantic parts or geometric primitives commonly used in 3D modeling. Our experiments demonstrate that our method can extract more accurate boundaries that are closer to ground-truth ones compared to alternatives. We also demonstrate an application of our network to fine-grained semantic shape segmentation, where we also show improvements in terms of part labeling performance.