# Rethinking Transparent Object Grasping: Depth Completion with Monocular Depth Estimation and Instance Mask

arXiv: https://arxiv.org/abs/2508.02507 · HTML: https://mlsanity.com/paper/2508.02507/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 29 of its 49 references that are in the map.
- First posted on arXiv 4 Aug 2025.

## Builds on (most cited first, 29 of 29)

- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [High-Resolution Image Synthesis with Latent Diffusion Models](https://mlsanity.com/paper/2112.10752/) (2021)
- [Swin Transformer: Hierarchical Vision Transformer using Shifted Windows](https://mlsanity.com/paper/2103.14030/) (2021)
- [Segment Anything](https://mlsanity.com/paper/2304.02643/) (2023)
- [PointNet: Deep Learning on Point Sets for 3D Classification and Segmentation](https://mlsanity.com/paper/1612.00593/) (2016)
- [PointNet++: Deep Hierarchical Feature Learning on Point Sets in a Metric Space](https://mlsanity.com/paper/1706.02413/) (2017)
- [Towards Robust Monocular Depth Estimation: Mixing Datasets for Zero-shot Cross-dataset Transfer](https://mlsanity.com/paper/1907.01341/) (2019)
- [Depth Anything V2](https://mlsanity.com/paper/2406.09414/) (2024)
- [Depth Map Prediction from a Single Image using a Multi-Scale Deep Network](https://mlsanity.com/paper/1406.2283/) (2014)
- [Deep Ordinal Regression Network for Monocular Depth Estimation](https://mlsanity.com/paper/1806.02446/) (2018)
- [Metric3D: Towards Zero-shot Metric 3D Prediction from A Single Image](https://mlsanity.com/paper/2307.10984/) (2023)
- [6-DOF GraspNet: Variational Grasp Generation for Object Manipulation](https://mlsanity.com/paper/1905.10520/) (2019)
- [Contact-GraspNet: Efficient 6-DoF Grasp Generation in Cluttered Scenes](https://mlsanity.com/paper/2103.14127/) (2021)
- [ClearGrasp: 3D Shape Estimation of Transparent Objects for Manipulation](https://mlsanity.com/paper/1910.02550/) (2019)
- [Deep Learning for Detecting Robotic Grasps](https://mlsanity.com/paper/1301.3592/) (2013)
- [PointNetGPD: Detecting Grasp Configurations from Point Sets](https://mlsanity.com/paper/1809.06267/) (2018)
- [GraspNeRF: Multiview-based 6-DoF Grasp Detection for Transparent and Specular Objects Using Generalizable NeRF](https://mlsanity.com/paper/2210.06575/) (2022)
- [RGB-D Local Implicit Function for Depth Completion of Transparent Objects](https://mlsanity.com/paper/2104.00622/) (2021)
- [TransCG: A Large-Scale Real-World Dataset for Transparent Object Depth Completion and a Grasping Baseline](https://mlsanity.com/paper/2202.08471/) (2022)
- [Domain Randomization-Enhanced Depth Simulation and Restoration for Perceiving and Grasping Specular and Transparent Objects](https://mlsanity.com/paper/2208.03792/) (2022)
- [Graspness Discovery in Clutters for Fast and Accurate Grasp Detection](https://mlsanity.com/paper/2406.11142/) (2024)
- [Seeing Glass: Joint Point Cloud and Depth Completion for Transparent Objects](https://mlsanity.com/paper/2110.00087/) (2021)
- [A4T: Hierarchical Affordance Detection for Transparent Objects Depth Reconstruction and Manipulation](https://mlsanity.com/paper/2207.04907/) (2022)
- [SharpDepth: Sharpening Metric Depth Predictions Using Diffusion Distillation](https://mlsanity.com/paper/2411.18229/) (2024)
- [TODE-Trans: Transparent Object Depth Estimation with Transformer](https://mlsanity.com/paper/2209.08455/) (2022)
- [FDCT: Fast Depth Completion for Transparent Objects](https://mlsanity.com/paper/2307.12274/) (2023)
- [GlassLoc: Plenoptic Grasp Pose Detection in Transparent Clutter](https://mlsanity.com/paper/1909.04269/) (2019)
- [TDCNet: Transparent Objects Depth Completion with CNN-Transformer Dual-Branch Parallel Network](https://mlsanity.com/paper/2412.14961/) (2024)
- [PCF-Grasp: Converting Point Completion to Geometry Feature to Enhance 6-DoF Grasp](https://mlsanity.com/paper/2504.16320/) (2025)

## Abstract (arXiv)

Due to the optical properties, transparent objects often lead depth cameras to generate incomplete or invalid depth data, which in turn reduces the accuracy and reliability of robotic grasping. Existing approaches typically input the RGB-D image directly into the network to output the complete depth, expecting the model to implicitly infer the reliability of depth values. However, while effective in training datasets, such methods often fail to generalize to real-world scenarios, where complex light interactions lead to highly variable distributions of valid and invalid depth data. To address this, we propose ReMake, a novel depth completion framework guided by an instance mask and monocular depth estimation. By explicitly distinguishing transparent regions from non-transparent ones, the mask enables the model to concentrate on learning accurate depth estimation in these areas from RGB-D input during training. This targeted supervision reduces reliance on implicit reasoning and improves generalization to real-world scenarios. Additionally, monocular depth estimation provides depth context between the transparent object and its surroundings, enhancing depth prediction accuracy. Extensive experiments show that our method outperforms existing approaches on both benchmark datasets and real-world scenarios, demonstrating superior accuracy and generalization capability. Code and videos are available at https://chengyaofeng.github.io/ReMake.github.io/.