# LIT: Light-field Inference of Transparency for Refractive Object Localization

arXiv: https://arxiv.org/abs/1910.00721 · HTML: https://mlsanity.com/paper/1910.00721/

## Key facts

- Cited by 14 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 2 Oct 2019.

## Built on by (most cited first, 14 of 14)

- [Dex-NeRF: Using a Neural Radiance Field to Grasp Transparent Objects](https://mlsanity.com/paper/2110.14217/) (2021)
- [ClearPose: Large-scale Transparent Object Dataset and Benchmark](https://mlsanity.com/paper/2203.03890/) (2022)
- [Seeing Glass: Joint Point Cloud and Depth Completion for Transparent Objects](https://mlsanity.com/paper/2110.00087/) (2021)
- [TRansPose: Large-Scale Multispectral Dataset for Transparent Object](https://mlsanity.com/paper/2307.05016/) (2023)
- [TransNet: Category-Level Transparent Object Pose Estimation](https://mlsanity.com/paper/2208.10002/) (2022)
- [FDCT: Fast Depth Completion for Transparent Objects](https://mlsanity.com/paper/2307.12274/) (2023)
- [LayeredFlow: A Real-World Benchmark for Non-Lambertian Multi-Layer Optical Flow](https://mlsanity.com/paper/2409.05688/) (2024)
- [Seeing and Seeing Through the Glass: Real and Synthetic Data for Multi-Layer Depth Estimation](https://mlsanity.com/paper/2503.11633/) (2025)
- [Visual-tactile Fusion for Transparent Object Grasping in Complex Backgrounds](https://mlsanity.com/paper/2211.16693/) (2022)
- [TransNet: Transparent Object Manipulation Through Category-Level Pose Estimation](https://mlsanity.com/paper/2307.12400/) (2023)
- [Tabletop Transparent Scene Reconstruction via Epipolar-Guided Optical Flow with Monocular Depth Completion Prior](https://mlsanity.com/paper/2310.09956/) (2023)
- [VAIR: Visuo-Acoustic Implicit Representations for Low-Cost, Multi-Modal Transparent Surface Reconstruction in Indoor Scenes](https://mlsanity.com/paper/2411.04963/) (2024)
- [Semantic Segmentation of Transparent and Opaque Drinking Glasses with the Help of Zero-shot Learning](https://mlsanity.com/paper/2503.15004/) (2025)
- [SeeGroup: Multi-Layer Depth Estimation of Transparent Surfaces via Self-Determined Grouping](https://mlsanity.com/paper/2605.28735/) (2026)

## Abstract (arXiv)

Translucency is prevalent in everyday scenes. As such, perception of transparent objects is essential for robots to perform manipulation. Compared with texture-rich or texture-less Lambertian objects, transparency induces significant uncertainty on object appearances. Ambiguity can be due to changes in lighting, viewpoint, and backgrounds, each of which brings challenges to existing object pose estimation algorithms. In this work, we propose LIT, a two-stage method for transparent object pose estimation using light-field sensing and photorealistic rendering. LIT employs multiple filters specific to light-field imagery in deep networks to capture transparent material properties, with robust depth and pose estimators based on generative sampling. Along with the LIT algorithm, we introduce the light-field transparent object dataset ProLIT for the tasks of recognition, localization and pose estimation. With respect to this ProLIT dataset, we demonstrate that LIT can outperform both state-of-the-art end-to-end pose estimation methods and a generative pose estimator on transparent objects.