Automatic identification of window regions on indoor point clouds using LiDAR and cameras

Richard Yi Zhang, Avideh Zakhor

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

In this paper, we propose an algorithm to automatically identify window regions on exterior facing facades of buildings using interior 3D point cloud resulting from an ambulatory backpack sensor system, outfitted with multiple LiDAR sensors and cameras. We develop a set of discriminative features for the task, namely visual brightness, infrared opaqueness, and an occlusion indicator, within a Markov Random Field (MRF) framework to provide structured prediction for window or glass regions. A preprocessing classifier is trained on the features to produce node potentials, and large margin parameter training is used to boost performance. Our algorithm has been trained on data taken at the 3rd floor of Cory Hall at UC Berkeley, with a total façade area of 269.1 m2, and has been tested on walls taken on the 2 nd floor of Cory Hall, a Walgreens, and an office building in San Francisco, with a total exterior façade area of 454.6 m2. Window regions are successfully identified with 85.5% F1-score and 94.2% accuracy.

Original languageEnglish (US)
Title of host publication2014 IEEE Winter Conference on Applications of Computer Vision, WACV 2014
PublisherIEEE Computer Society
Pages107-114
Number of pages8
ISBN (Print)9781479949854
DOIs
StatePublished - Jan 1 2014
Externally publishedYes
Event2014 IEEE Winter Conference on Applications of Computer Vision, WACV 2014 - Steamboat Springs, CO, United States
Duration: Mar 24 2014Mar 26 2014

Publication series

Name2014 IEEE Winter Conference on Applications of Computer Vision, WACV 2014

Other

Other2014 IEEE Winter Conference on Applications of Computer Vision, WACV 2014
CountryUnited States
CitySteamboat Springs, CO
Period3/24/143/26/14

ASJC Scopus subject areas

  • Computer Science Applications
  • Computer Vision and Pattern Recognition

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