Automatic Modeling of Urban Facades from Raw LiDAR Point Data

Modeling of urban facades from raw LiDAR point data remains active due to its challenging nature. In this paper, we propose an automatic yet robust 3D modeling approach for urban facades with raw LiDAR point clouds. The key observation is that building facades often exhibit repetitions and regularit...

Full description

Saved in:
Bibliographic Details
Published in:Computer graphics forum Vol. 35; no. 7; pp. 269 - 278
Main Authors: Wang, J., Xu, Y., Remil, O., Xie, X., Ye, N., Yi, C., Wei, M.
Format: Journal Article
Language:English
Published: Oxford Blackwell Publishing Ltd 01.10.2016
Subjects:
3-D graphics
Analysis
and systems
Automation
Buildings
Categories and Subject Descriptors (according to ACM CCS)
Computer based modeling
Facades
I.3.5 [Computer Graphics]: Computational Geometry and Object Modeling-Geometric algorithms
I.3.5 [Computer Graphics]: Computational Geometry and Object Modeling—Geometric algorithms, languages, and systems
languages
Lidar
Measurement
Modelling
Optimization
Raw
Regularity
Repetition
Studies
Three dimensional models
Urban areas
ISSN:0167-7055, 1467-8659
Online Access:Get full text
Tags: Add Tag
No Tags, Be the first to tag this record!
Description
Summary:Modeling of urban facades from raw LiDAR point data remains active due to its challenging nature. In this paper, we propose an automatic yet robust 3D modeling approach for urban facades with raw LiDAR point clouds. The key observation is that building facades often exhibit repetitions and regularities. We hereby formulate repetition detection as an energy optimization problem with a global energy function balancing geometric errors, regularity and complexity of facade structures. As a result, repetitive structures are extracted robustly even in the presence of noise and missing data. By registering repetitive structures, missing regions are completed and thus the associated point data of structures are well consolidated. Subsequently, we detect the potential design intents (i.e., geometric constraints) within structures and perform constrained fitting to obtain the precise structure models. Furthermore, we apply structure alignment optimization to enforce position regularities and employ repetitions to infer missing structures. We demonstrate how the quality of raw LiDAR data can be improved by exploiting data redundancy, and discovering high level structural information (regularity and symmetry). We evaluate our modeling method on a variety of raw LiDAR scans to verify its robustness and effectiveness.
Bibliography:ark:/67375/WNG-0GQG4JG7-S
istex:C5B4E1C1AB0F65CE785096D9902D80A51AC472DD
ArticleID:CGF13024
SourceType-Scholarly Journals-1
ObjectType-Feature-1
content type line 14
ObjectType-Article-1
ObjectType-Feature-2
content type line 23
ISSN:0167-7055
1467-8659
DOI:10.1111/cgf.13024