3D modeling of building indoor spaces and closed doors from imagery and point clouds

Lucía Díaz-Vilariño*, Kourosh Khoshelham, Joaquín Martínez-Sánchez, Pedro Arias

*Corresponding author for this work

Research output: Contribution to journalArticleAcademicpeer-review

88 Citations (Scopus)
17 Downloads (Pure)


3D models of indoor environments are increasingly gaining importance due to the wide range of applications to which they can be subjected: from redesign and visualization to monitoring and simulation. These models usually exist only for newly constructed buildings; therefore, the development of automatic approaches for reconstructing 3D indoors from imagery and/or point clouds can make the process easier, faster and cheaper. Among the constructive elements defining a building interior, doors are very common elements and their detection can be very useful either for knowing the environment structure, to perform an efficient navigation or to plan appropriate evacuation routes. The fact that doors are topologically connected to walls by being coplanar, together with the unavoidable presence of clutter and occlusions indoors, increases the inherent complexity of the automation of the recognition process. In this work, we present a pipeline of techniques used for the reconstruction and interpretation of building interiors based on point clouds and images. The methodology analyses the visibility problem of indoor environments and goes in depth with door candidate detection. The presented approach is tested in real data sets showing its potential with a high door detection rate and applicability for robust and efficient envelope reconstruction.
Original languageEnglish
Pages (from-to)3491-3512
Number of pages22
JournalSensors (Switzerland)
Issue number2
Publication statusPublished - 3 Feb 2015


  • 3D modeling
  • feature extraction
  • openings
  • imagery
  • LiDAR data
  • BIM


Dive into the research topics of '3D modeling of building indoor spaces and closed doors from imagery and point clouds'. Together they form a unique fingerprint.

Cite this