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Implicit Regularization for Reconstructing 3D Building Rooftop Models Using Airborne LiDAR Data.

Jaewook Jung1, Yoonseok Jwa2, Gunho Sohn3

  • 1Department of Earth and Space Science and Engineering, York University, 4700 Keele Street, Toronto M3J 1P3, ON, Canada. jwjung00@gmail.com.

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Summary
This summary is machine-generated.

This study presents a novel data-driven approach for reconstructing accurate 3D building rooftop models from airborne laser scanning data. The method effectively regularizes noisy boundaries to create detailed city-scale models for urban applications.

Keywords:
3D building rooftop modelingairborne laser scanning databuilding reconstructionminimum description lengthregularization

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Area of Science:

  • Geoinformatics
  • Computer Vision
  • 3D Reconstruction

Background:

  • Accurate 3D building models are crucial for urban planning and services.
  • Automated 3D reconstruction from remote sensing data faces challenges with noisy boundaries.
  • Existing methods struggle with massive generation of highly accurate building models.

Purpose of the Study:

  • To develop a data-driven method for reconstructing 3D rooftop models at city-scale.
  • To address the challenge of regularizing noisy building boundaries in 3D reconstruction.
  • To provide a full chain of 3D building modeling from data processing to realistic rooftop generation.

Main Methods:

  • Clustering of point clouds using height and plane similarity.
  • Extraction of linear modeling cues (boundaries, intersection, step lines).
  • Regularization using Minimum Description Length (MDL) and Hypothesize and Test (HAT) frameworks.

Main Results:

  • Robust reconstruction of accurate, regularized 3D building rooftop models.
  • Successful application on International Society for Photogrammetry and Remote Sensing (ISPRS) benchmark datasets.
  • Implicit derivation of rooftop shape regularity from noisy boundary data.

Conclusions:

  • The proposed method offers a robust solution for city-scale 3D building rooftop modeling.
  • The data-driven approach effectively handles noisy data for improved model accuracy.
  • This work advances automated 3D reconstruction for urban applications.