Recognition and Reconstruction of Transparent Objects for Augmented Reality

Alan Torres Gomez, Walterio W Mayol-Cuevas

Research output: Chapter in Book/Report/Conference proceedingConference Contribution (Conference Proceeding)

8 Citations (Scopus)
446 Downloads (Pure)


Dealing with real transparent objects for AR is challenging due to their lack of texture and visual features as well as the drastic changes in appearance as the background, illumination and camera pose change. The few existing methods for glass object detection usually require a carefully controlled environment, specialized illumination hardware or ignore information from different viewpoints.

In this work, we explore the use of a learning approach for classifying transparent objects from multiple images with the aim of both discovering such objects and building a 3D reconstruction to support convincing augmentations. We extract, classify and group small image patches using a fast graph-based segmentation and employ a probabilistic formulation for aggregating spatially consistent glass regions. We demonstrate our approach via analysis of the performance of glass region detection and example 3D reconstructions that allow virtual objects to interact with them.
Original languageEnglish
Title of host publication2014 IEEE International Symposium on Mixed and Augmented Reality (ISMAR 2014)
Subtitle of host publicationProceedings of a meeting held 10-12 September 2014, Munich, Germany
PublisherInstitute of Electrical and Electronics Engineers (IEEE)
Number of pages6
ISBN (Electronic)9781479961849
ISBN (Print)9781479961856
Publication statusPublished - Dec 2014


  • Augmented Reality
  • Glass detection
  • 3D Reconstruction
  • Computer Vision


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