Title
Multiple View Based Building Modeling with Multi-box Grammar
Abstract
This paper describes a multiple view based approach for building modeling via a novel multi-box grammar, which represents an occlusion relationship among the projections of a set of buildings sharing a common Manhattan World coordinate system. We formulate the building modeling problem as an energy minimization to combine the constraints from the multi-box grammar with (1) the semantic labeling information from appearance models, (2) the directional information w.r.t the vanishing points in each single view, and (3) the planar homography correspondence among multiple views. We further propose a two-step coarse-to-fine approach to achieve the optimal solution. First we employ super-pixels and a simplified edition of the grammar to reduce the searching space, and obtain an initial layout to accelerate the convergence speed. At the second stage, the scene model is refined to achieve pixel-level accuracy by minimizing the energy using Random Walk. Experiments on street view images demonstrate the capability of our method in reconstructing multiple buildings at different distances, and also the robustness in handling occlusion.
Year
DOI
Venue
2014
10.1109/ICPR.2014.690
ICPR
Keywords
Field
DocType
manhattan world coordinate system,optimal solution,civil engineering computing,super-pixels,multiple view based building modeling,multibox grammar,random walk,pixel-level accuracy,energy minimization,grammars,solid modelling,two-step coarse-to-fine approach,buildings (structures),appearance models,planar homography correspondence,occlusion relationship,building modeling problem,solid modeling,shape,computational modeling,grammar,image reconstruction
Coordinate system,Iterative reconstruction,Convergence (routing),Computer vision,Computer science,Grammar,Robustness (computer science),Artificial intelligence,Solid modeling,Vanishing point,Energy minimization
Conference
ISSN
Citations 
PageRank 
1051-4651
0
0.34
References 
Authors
12
5
Name
Order
Citations
PageRank
Ruiling Deng110.75
Qiuliang Wang200.34
Rui Gan318313.62
Gang Zeng494970.21
Hongbin Zha52206183.36