# TransCut: Transparent Object Segmentation from a Light-Field Image

arXiv: https://arxiv.org/abs/1511.06853 · HTML: https://mlsanity.com/paper/1511.06853/

## Key facts

- Cited by 27 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 21 Nov 2015.

## Built on by (most cited first, 27 of 27)

- [Concealed Object Detection](https://mlsanity.com/paper/2102.10274/) (2021)
- [ClearPose: Large-scale Transparent Object Dataset and Benchmark](https://mlsanity.com/paper/2203.03890/) (2022)
- [Glass Segmentation with RGB-Thermal Image Pairs](https://mlsanity.com/paper/2204.05453/) (2022)
- [RFENet: Towards Reciprocal Feature Evolution for Glass Segmentation](https://mlsanity.com/paper/2307.06099/) (2023)
- [TRansPose: Large-Scale Multispectral Dataset for Transparent Object](https://mlsanity.com/paper/2307.05016/) (2023)
- [Segmenting Transparent Object in the Wild with Transformer](https://mlsanity.com/paper/2101.08461/) (2021)
- [TransNet: Category-Level Transparent Object Pose Estimation](https://mlsanity.com/paper/2208.10002/) (2022)
- [LayeredFlow: A Real-World Benchmark for Non-Lambertian Multi-Layer Optical Flow](https://mlsanity.com/paper/2409.05688/) (2024)
- [Self-supervised Transparent Liquid Segmentation for Robotic Pouring](https://mlsanity.com/paper/2203.01538/) (2022)
- [Seeing and Seeing Through the Glass: Real and Synthetic Data for Multi-Layer Depth Estimation](https://mlsanity.com/paper/2503.11633/) (2025)
- [FakeMix Augmentation Improves Transparent Object Detection](https://mlsanity.com/paper/2103.13279/) (2021)
- [Deep Learning in Concealed Dense Prediction](https://mlsanity.com/paper/2504.10979/) (2025)
- [Trans2k: Unlocking the Power of Deep Models for Transparent Object Tracking](https://mlsanity.com/paper/2210.03436/) (2022)
- [A New Dataset and a Distractor-Aware Architecture for Transparent Object Tracking](https://mlsanity.com/paper/2401.03872/) (2024)
- [GEM: Boost Simple Network for Glass Surface Segmentation via Vision Foundation Models](https://mlsanity.com/paper/2307.12018/) (2023)
- [Learning Kernel-Modulated Neural Representation for Efficient Light Field Compression](https://mlsanity.com/paper/2307.06143/) (2023)
- [A Comparison for Patch-level Classification of Deep Learning Methods on Transparent Environmental Microorganism Images: from Convolutional Neural Networks to Visual Transformers](https://mlsanity.com/paper/2106.11582/) (2021)
- [Grasping the Inconspicuous](https://mlsanity.com/paper/2211.08182/) (2022)
- [TransNet: Transparent Object Manipulation Through Category-Level Pose Estimation](https://mlsanity.com/paper/2307.12400/) (2023)
- [Transparent Object Tracking with Enhanced Fusion Module](https://mlsanity.com/paper/2309.06701/) (2023)
- [Influence of Water Droplet Contamination for Transparency Segmentation](https://mlsanity.com/paper/2405.12861/) (2024)
- [Refractive Light-Field Features for Curved Transparent Objects in Structure from Motion](https://mlsanity.com/paper/2103.15349/) (2021)
- [Learning-based Spatial and Angular Information Separation for Light Field Compression](https://mlsanity.com/paper/2304.06322/) (2023)
- [Semantic Segmentation of Transparent and Opaque Drinking Glasses with the Help of Zero-shot Learning](https://mlsanity.com/paper/2503.15004/) (2025)
- [Glass Surface Detection: Leveraging Reflection Dynamics in Flash/No-flash Imagery](https://mlsanity.com/paper/2511.16887/) (2025)
- [Transparent Fragments Contour Estimation via Visual-Tactile Fusion for Autonomous Reassembly](https://mlsanity.com/paper/2603.20290/) (2026)
- [SeeGroup: Multi-Layer Depth Estimation of Transparent Surfaces via Self-Determined Grouping](https://mlsanity.com/paper/2605.28735/) (2026)

## Abstract (arXiv)

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.