# PC2WF: 3D Wireframe Reconstruction from Raw Point Clouds

arXiv: https://arxiv.org/abs/2103.02766 · HTML: https://mlsanity.com/paper/2103.02766/

## Key facts

- Cited by 33 papers in the map (data through 14 Oct 2026).
- Builds on 19 of its 49 references that are in the map.
- First posted on arXiv 4 Mar 2021.

## Builds on (most cited first, 19 of 19)

- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [Adam: A Method for Stochastic Optimization](https://mlsanity.com/paper/1412.6980/) (2014)
- [U-Net: Convolutional Networks for Biomedical Image Segmentation](https://mlsanity.com/paper/1505.04597/) (2015)
- [Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks](https://mlsanity.com/paper/1506.01497/) (2015)
- [4D Spatio-Temporal ConvNets: Minkowski Convolutional Neural Networks](https://mlsanity.com/paper/1904.08755/) (2019)
- [ABC: A Big CAD Model Dataset For Geometric Deep Learning](https://mlsanity.com/paper/1812.06216/) (2018)
- [Learning Shape Abstractions by Assembling Volumetric Primitives](https://mlsanity.com/paper/1612.00404/) (2016)
- [CSGNet: Neural Shape Parser for Constructive Solid Geometry](https://mlsanity.com/paper/1712.08290/) (2017)
- [LayoutNet: Reconstructing the 3D Room Layout from a Single RGB Image](https://mlsanity.com/paper/1803.08999/) (2018)
- [Supervised Fitting of Geometric Primitives to 3D Point Clouds](https://mlsanity.com/paper/1811.08988/) (2018)
- [EC-Net: an Edge-aware Point set Consolidation Network](https://mlsanity.com/paper/1807.06010/) (2018)
- [3D-PRNN: Generating Shape Primitives with Recurrent Neural Networks](https://mlsanity.com/paper/1708.01648/) (2017)
- [End-to-End Wireframe Parsing](https://mlsanity.com/paper/1905.03246/) (2019)
- [Learning to Parse Wireframes in Images of Man-Made Environments](https://mlsanity.com/paper/2007.07527/) (2020)
- [PIE-NET: Parametric Inference of Point Cloud Edges](https://mlsanity.com/paper/2007.04883/) (2020)
- [Holistically-Attracted Wireframe Parsing](https://mlsanity.com/paper/2003.01663/) (2020)
- [Learning to Reconstruct 3D Manhattan Wireframes from a Single Image](https://mlsanity.com/paper/1905.07482/) (2019)
- [Fast Resampling of 3D Point Clouds via Graphs](https://mlsanity.com/paper/1702.06397/) (2017)
- [Edge and Corner Detection for Unorganized 3D Point Clouds with Application to Robotic Welding](https://mlsanity.com/paper/1809.10468/) (2018)

## Built on by (most cited first, 30 of 33)

- [CAD-Recode: Reverse Engineering CAD Code from Point Clouds](https://mlsanity.com/paper/2412.14042/) (2024)
- [CAD-SIGNet: CAD Language Inference from Point Clouds using Layer-wise Sketch Instance Guided Attention](https://mlsanity.com/paper/2402.17678/) (2024)
- [TransCAD: A Hierarchical Transformer for CAD Sequence Inference from Point Clouds](https://mlsanity.com/paper/2407.12702/) (2024)
- [Building3D: An Urban-Scale Dataset and Benchmarks for Learning Roof Structures from Point Clouds](https://mlsanity.com/paper/2307.11914/) (2023)
- [NEF: Neural Edge Fields for 3D Parametric Curve Reconstruction from Multi-view Images](https://mlsanity.com/paper/2303.07653/) (2023)
- [NerVE: Neural Volumetric Edges for Parametric Curve Extraction from Point Cloud](https://mlsanity.com/paper/2303.16465/) (2023)
- [Sketch and Patch: Efficient 3D Gaussian Representation for Man-Made Scenes](https://mlsanity.com/paper/2501.13045/) (2025)
- [ComplexGen: CAD Reconstruction by B-Rep Chain Complex Generation](https://mlsanity.com/paper/2205.14573/) (2022)
- [SepicNet: Sharp Edges Recovery by Parametric Inference of Curves in 3D Shapes](https://mlsanity.com/paper/2304.06531/) (2023)
- [HoW-3D: Holistic 3D Wireframe Perception from a Single Image](https://mlsanity.com/paper/2208.06999/) (2022)
- [3D Neural Edge Reconstruction](https://mlsanity.com/paper/2405.19295/) (2024)
- [EdgeGaussians -- 3D Edge Mapping via Gaussian Splatting](https://mlsanity.com/paper/2409.12886/) (2024)
- [Curve-Aware Gaussian Splatting for 3D Parametric Curve Reconstruction](https://mlsanity.com/paper/2506.21401/) (2025)
- [Learning to Construct 3D Building Wireframes from 3D Line Clouds](https://mlsanity.com/paper/2208.11948/) (2022)
- [Generating 3D House Wireframes with Semantics](https://mlsanity.com/paper/2407.12267/) (2024)
- [CLR-Wire: Towards Continuous Latent Representations for 3D Curve Wireframe Generation](https://mlsanity.com/paper/2504.19174/) (2025)
- [PBWR: Parametric Building Wireframe Reconstruction from Aerial LiDAR Point Clouds](https://mlsanity.com/paper/2311.12062/) (2023)
- [3D Parametric Wireframe Extraction Based on Distance Fields](https://mlsanity.com/paper/2107.06165/) (2021)
- [MV2Cyl: Reconstructing 3D Extrusion Cylinders from Multi-View Images](https://mlsanity.com/paper/2406.10853/) (2024)
- [Explaining Human Preferences via Metrics for Structured 3D Reconstruction](https://mlsanity.com/paper/2503.08208/) (2025)
- [APC2Mesh: Bridging the gap from occluded building faccades to full 3D models](https://mlsanity.com/paper/2404.02391/) (2024)
- [LineGS : 3D Line Segment Representation on 3D Gaussian Splatting](https://mlsanity.com/paper/2412.00477/) (2024)
- [PI3DETR: Parametric Instance Detection of 3D Point Cloud Edges With a Geometry-Aware 3DETR](https://mlsanity.com/paper/2509.03262/) (2025)
- [PRS: Sharp Feature Priors for Resolution-Free Surface Remeshing](https://mlsanity.com/paper/2311.18494/) (2023)
- [Sketch&Patch++: Efficient Structure-Aware 3D Gaussian Representation](https://mlsanity.com/paper/2601.05394/) (2026)
- [3DrawAgent: Teaching LLM to Draw in 3D with Early Contrastive Experience](https://mlsanity.com/paper/2604.08042/) (2026)
- [LapisGS: Layered Progressive 3D Gaussian Splatting for Adaptive Streaming](https://mlsanity.com/paper/2408.14823/) (2024)
- [STAR-Edge: Structure-aware Local Spherical Curve Representation for Thin-walled Edge Extraction from Unstructured Point Clouds](https://mlsanity.com/paper/2503.00801/) (2025)
- [Detecting 3D Line Segments for 6DoF Pose Estimation with Limited Data](https://mlsanity.com/paper/2601.12090/) (2026)
- [Delaunay Canopy: Building Wireframe Reconstruction from Airborne LiDAR Point Clouds via Delaunay Graph](https://mlsanity.com/paper/2604.02497/) (2026)

## Abstract (arXiv)

We introduce PC2WF, the first end-to-end trainable deep network architecture to convert a 3D point cloud into a wireframe model. The network takes as input an unordered set of 3D points sampled from the surface of some object, and outputs a wireframe of that object, i.e., a sparse set of corner points linked by line segments. Recovering the wireframe is a challenging task, where the numbers of both vertices and edges are different for every instance, and a-priori unknown. Our architecture gradually builds up the model: It starts by encoding the points into feature vectors. Based on those features, it identifies a pool of candidate vertices, then prunes those candidates to a final set of corner vertices and refines their locations. Next, the corners are linked with an exhaustive set of candidate edges, which is again pruned to obtain the final wireframe. All steps are trainable, and errors can be backpropagated through the entire sequence. We validate the proposed model on a publicly available synthetic dataset, for which the ground truth wireframes are accessible, as well as on a new real-world dataset. Our model produces wireframe abstractions of good quality and outperforms several baselines.