Data-driven facade reconstruction

Fubo Han, Yunhai Wang, Liangliang Nan, Baoquan Chen*

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

Abstract

We proposed a new data-driven method to infer depth information from a single facade image. A facade is firstly segmented into several regions. By exploiting the symmetry characteristics of facade elements (i.e., windows), we segment the facade image using a Markov random field (MRF) formulation. We represent each facade by a graph, in which each graph node represents a segmented image region with consistent appearance, and each graph edge encodes the spatial relationship between two distinct image regions. Then we generate a semantic label for each region by automatically matching the graph with our predefined templates in the database. Finally, we perform a global optimization process to produce the final facade model. Experiments demonstrate that our approach can generate favorable results.

Original languageEnglish (US)
Pages (from-to)2025-2030
Number of pages6
JournalJisuanji Fuzhu Sheji Yu Tuxingxue Xuebao/Journal of Computer-Aided Design and Computer Graphics
Volume27
Issue number11
StatePublished - Nov 1 2015

Keywords

  • Depth recovery
  • Facade reconstruction
  • Graphical model
  • Markov random field

ASJC Scopus subject areas

  • Software
  • Computer Graphics and Computer-Aided Design

Fingerprint Dive into the research topics of 'Data-driven facade reconstruction'. Together they form a unique fingerprint.

Cite this