# GlassLoc: Plenoptic Grasp Pose Detection in Transparent Clutter

arXiv: https://arxiv.org/abs/1909.04269 · HTML: https://mlsanity.com/paper/1909.04269/

## Key facts

- Cited by 11 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 10 Sep 2019.

## Built on by (most cited first, 11 of 11)

- [Dex-NeRF: Using a Neural Radiance Field to Grasp Transparent Objects](https://mlsanity.com/paper/2110.14217/) (2021)
- [GraspNeRF: Multiview-based 6-DoF Grasp Detection for Transparent and Specular Objects Using Generalizable NeRF](https://mlsanity.com/paper/2210.06575/) (2022)
- [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)
- [Visual-tactile Fusion for Transparent Object Grasping in Complex Backgrounds](https://mlsanity.com/paper/2211.16693/) (2022)
- [STOPNet: Multiview-based 6-DoF Suction Detection for Transparent Objects on Production Lines](https://mlsanity.com/paper/2310.05717/) (2023)
- [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)
- [Enhancing Transparent Object Pose Estimation: A Fusion of GDR-Net and Edge Detection](https://mlsanity.com/paper/2502.12027/) (2025)
- [Rethinking Transparent Object Grasping: Depth Completion with Monocular Depth Estimation and Instance Mask](https://mlsanity.com/paper/2508.02507/) (2025)
- [Transparent Fragments Contour Estimation via Visual-Tactile Fusion for Autonomous Reassembly](https://mlsanity.com/paper/2603.20290/) (2026)

## Abstract (arXiv)

Transparent objects are prevalent across many environments of interest for dexterous robotic manipulation. Such transparent material leads to considerable uncertainty for robot perception and manipulation, and remains an open challenge for robotics. This problem is exacerbated when multiple transparent objects cluster into piles of clutter. In household environments, for example, it is common to encounter piles of glassware in kitchens, dining rooms, and reception areas, which are essentially invisible to modern robots. We present the GlassLoc algorithm for grasp pose detection of transparent objects in transparent clutter using plenoptic sensing. GlassLoc classifies graspable locations in space informed by a Depth Likelihood Volume (DLV) descriptor. We extend the DLV to infer the occupancy of transparent objects over a given space from multiple plenoptic viewpoints. We demonstrate and evaluate the GlassLoc algorithm on a Michigan Progress Fetch mounted with a first-generation Lytro. The effectiveness of our algorithm is evaluated through experiments for grasp detection and execution with a variety of transparent glassware in minor clutter.