TransCut: Transparent Object Segmentation from a Light-Field Image

Yichao Xu, Hajime Nagahara, Atsushi Shimada, Rin-ichiro Taniguchi

Kyushu University, Japan


The segmentation of transparent objects can be very useful in computer vision applications. However, because they borrow texture from their background and have a similar appearance to their surroundings, transparent objects are not handled well by regular image segmentation methods. We propose a method that overcomes these problems using the consistency and distortion properties of a light-field image. Graph-cut optimization is applied for the pixel labeling problem. The light-field linearity is used to estimate the likelihood of a pixel belonging to the transparent object or Lambertian background, and the occlusion detector is used to find the occlusion boundary. We acquire a light field dataset for the transparent object, and use this dataset to evaluate our method. The results demonstrate that the proposed method successfully segments transparent objects from the background.

arXiv pre-print

Supplemetary material

ICCV poster

Please cite
author = {Yichao Xu and Hajime Nagahara and Atsushi Shimada and Rin-ichiro Taniguchi},
title = {TransCut: Transparent Object Segmentation from a Light-Field Image},
booktitle = {International Conference on Computer Vision (ICCV)},
year = {2015}


This dataset could be downloaded as a zip file with password protection and the password will be issued on a case-by-case basis. To receive the download link and the password, the requestor must send the release agreement signed by a legal representative of your institution (e.g., your supervisor if you are a student) to the dataset administrator by E-mail: nagahara(at)

Related Project

Light Field Vision