mlsanityData to 7 Oct 2026

PBWR: Parametric Building Wireframe Reconstruction from Aerial LiDAR Point Clouds

arXiv 2311.12062 ↗First posted 18 Nov 2023cs.CV cs.AIcited by 5

Not on the map. Why?

Abstract (arXiv)

In this paper, we present an end-to-end 3D building wireframe reconstruction method to regress edges directly from aerial LiDAR point clouds.Our method, named Parametric Building Wireframe Reconstruction (PBWR), takes aerial LiDAR point clouds and initial edge entities as input, and fully uses self-attention mechanism of transformers to regress edge parameters without any intermediate steps such as corner prediction. We propose an edge non-maximum suppression (E-NMS) module based on edge similarityto remove redundant edges. Additionally, a dedicated edge loss function is utilized to guide the PBWR in regressing edges parameters, where simple use of edge distance loss isn't suitable. In our experiments, we demonstrate state-of-the-art results on the Building3D dataset, achieving an improvement of approximately 36% in entry-level dataset edge accuracy and around 42% improvement in the Tallinn dataset.

Key facts. Cited by 5 papers in the map (data through 7 Oct 2026). Builds on 20 of its 43 references that are in the map. First posted on arXiv 18 Nov 2023.

Builds on

20 of 43 references.

References · 20

  1. 2017Attention Is All You Needcited by 59,932
  2. 2016PointNet: Deep Learning on Point Sets for 3D Classification and Segmentationcited by 5,223
  3. 2017PointNet++: Deep Hierarchical Feature Learning on Point Sets in a Metric Spacecited by 4,384
  4. 2020Point Transformercited by 1,462
  5. 2020PCT: Point cloud transformercited by 730
  6. 2019Deep Hough Voting for 3D Object Detection in Point Cloudscited by 648
  7. 2021Point-BERT: Pre-training 3D Point Cloud Transformers with Masked Point Modelingcited by 647
  8. 2019RandLA-Net: Efficient Semantic Segmentation of Large-Scale Point Cloudscited by 584
  9. 2022Masked Autoencoders for Point Cloud Self-supervised Learningcited by 456
  10. 2018ABC: A Big CAD Model Dataset For Geometric Deep Learningcited by 339
  11. 2021An End-to-End Transformer Model for 3D Object Detectioncited by 329
  12. 2021PAConv: Position Adaptive Convolution with Dynamic Kernel Assembling on Point Cloudscited by 211
8 more
  1. 2018EC-Net: an Edge-aware Point set Consolidation Networkcited by 84
  2. 2020PIE-NET: Parametric Inference of Point Cloud Edgescited by 67
  3. 2021PC2WF: 3D Wireframe Reconstruction from Raw Point Cloudscited by 33
  4. 2023Building3D: An Urban-Scale Dataset and Benchmarks for Learning Roof Structures from Point Cloudscited by 24
  5. 2022City3D: Large-Scale Building Reconstruction from Airborne LiDAR Point Cloudscited by 22
  6. 2023NerVE: Neural Volumetric Edges for Parametric Curve Extraction from Point Cloudcited by 22
  7. 2020PCEDNet : A Lightweight Neural Network for Fast and Interactive Edge Detection in 3D Point Cloudscited by 15
  8. 2022Learning to Construct 3D Building Wireframes from 3D Line Cloudscited by 9

Built on by

Cited by year (2026 to 7 Oct)

Citing papers per year: 2025: 2; 2026: 3.

5 papers.

  1. 2025Explaining Human Preferences via Metrics for Structured 3D Reconstructioncited by 3
  2. 2025SYNBUILD-3D: A large, multi-modal, and semantically rich synthetic dataset of 3D building models at Level of Detail 4cited by 0
  3. 2026PIRATR: Parametric Object Inference for Robotic Applications with Transformers in 3D Point Cloudscited by 0
  4. 2026Delaunay Canopy: Building Wireframe Reconstruction from Airborne LiDAR Point Clouds via Delaunay Graphcited by 0
  5. 2026Learned Suppression for 3D Keypoint Detection with a Graph-Transformer Backbonecited by 0