# FDCT: Fast Depth Completion for Transparent Objects

arXiv: https://arxiv.org/abs/2307.12274 · HTML: https://mlsanity.com/paper/2307.12274/

## Key facts

- Cited by 12 papers in the map (data through 14 Oct 2026).
- Builds on 24 of its 48 references that are in the map.
- First posted on arXiv 23 Jul 2023.

## Builds on (most cited first, 24 of 24)

- [U-Net: Convolutional Networks for Biomedical Image Segmentation](https://mlsanity.com/paper/1505.04597/) (2015)
- [Densely Connected Convolutional Networks](https://mlsanity.com/paper/1608.06993/) (2016)
- [Real-Time Single Image and Video Super-Resolution Using an Efficient Sub-Pixel Convolutional Neural Network](https://mlsanity.com/paper/1609.05158/) (2016)
- [Predicting Depth, Surface Normals and Semantic Labels with a Common Multi-Scale Convolutional Architecture](https://mlsanity.com/paper/1411.4734/) (2014)
- [DenseFusion: 6D Object Pose Estimation by Iterative Dense Fusion](https://mlsanity.com/paper/1901.04780/) (2019)
- [Single-Image Depth Perception in the Wild](https://mlsanity.com/paper/1604.03901/) (2016)
- [An Energy and GPU-Computation Efficient Backbone Network for Real-Time Object Detection](https://mlsanity.com/paper/1904.09730/) (2019)
- [PENet: Towards Precise and Efficient Image Guided Depth Completion](https://mlsanity.com/paper/2103.00783/) (2021)
- [ClearGrasp: 3D Shape Estimation of Transparent Objects for Manipulation](https://mlsanity.com/paper/1910.02550/) (2019)
- [Dex-NeRF: Using a Neural Radiance Field to Grasp Transparent Objects](https://mlsanity.com/paper/2110.14217/) (2021)
- [Deep Depth Completion of a Single RGB-D Image](https://mlsanity.com/paper/1803.09326/) (2018)
- [Divide-and-Assemble: Learning Block-wise Memory for Unsupervised Anomaly Detection](https://mlsanity.com/paper/2107.13118/) (2021)
- [RigNet: Repetitive Image Guided Network for Depth Completion](https://mlsanity.com/paper/2107.13802/) (2021)
- [Sparse and Dense Data with CNNs: Depth Completion and Semantic Segmentation](https://mlsanity.com/paper/1808.00769/) (2018)
- [KeyPose: Multi-View 3D Labeling and Keypoint Estimation for Transparent Objects](https://mlsanity.com/paper/1912.02805/) (2019)
- [RGB-D Local Implicit Function for Depth Completion of Transparent Objects](https://mlsanity.com/paper/2104.00622/) (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)
- [Indoor Depth Completion with Boundary Consistency and Self-Attention](https://mlsanity.com/paper/1908.08344/) (2019)
- [SemAttNet: Towards Attention-based Semantic Aware Guided Depth Completion](https://mlsanity.com/paper/2204.13635/) (2022)
- [PR-GCN: A Deep Graph Convolutional Network with Point Refinement for 6D Pose Estimation](https://mlsanity.com/paper/2108.09916/) (2021)
- [LIT: Light-field Inference of Transparency for Refractive Object Localization](https://mlsanity.com/paper/1910.00721/) (2019)
- [GlassLoc: Plenoptic Grasp Pose Detection in Transparent Clutter](https://mlsanity.com/paper/1909.04269/) (2019)
- [ProgressLabeller: Visual Data Stream Annotation for Training Object-Centric 3D Perception](https://mlsanity.com/paper/2203.00283/) (2022)

## Built on by (most cited first, 12 of 12)

- [ClearDepth: Enhanced Stereo Perception of Transparent Objects for Robotic Manipulation](https://mlsanity.com/paper/2409.08926/) (2024)
- [Diffusion-Based Depth Inpainting for Transparent and Reflective Objects](https://mlsanity.com/paper/2410.08567/) (2024)
- [DeLTa: Demonstration and Language-Guided Novel Transparent Object Manipulation](https://mlsanity.com/paper/2510.05662/) (2025)
- [Transparent Object Depth Completion](https://mlsanity.com/paper/2405.15299/) (2024)
- [DistillGrasp: Integrating Features Correlation with Knowledge Distillation for Depth Completion of Transparent Objects](https://mlsanity.com/paper/2408.00337/) (2024)
- [Self-Supervised Learning for Transparent Object Depth Completion Using Depth from Non-Transparent Objects](https://mlsanity.com/paper/2512.05006/) (2025)
- [Gaussian Belief Propagation Network for Depth Completion](https://mlsanity.com/paper/2601.21291/) (2026)
- [GAA-TSO: Geometry-Aware Assisted Depth Completion for Transparent and Specular Objects](https://mlsanity.com/paper/2503.17106/) (2025)
- [HTMNet: A Hybrid Network with Transformer-Mamba Bottleneck Multimodal Fusion for Transparent and Reflective Objects Depth Completion](https://mlsanity.com/paper/2505.20904/) (2025)
- [DCIRNet: Depth Completion with Iterative Refinement for Dexterous Grasping of Transparent and Reflective Objects](https://mlsanity.com/paper/2506.09491/) (2025)
- [DidSee: Diffusion-Based Depth Completion for Material-Agnostic Robotic Perception and Manipulation](https://mlsanity.com/paper/2506.21034/) (2025)
- [Rethinking Transparent Object Grasping: Depth Completion with Monocular Depth Estimation and Instance Mask](https://mlsanity.com/paper/2508.02507/) (2025)

## Abstract (arXiv)

Depth completion is crucial for many robotic tasks such as autonomous driving, 3-D reconstruction, and manipulation. Despite the significant progress, existing methods remain computationally intensive and often fail to meet the real-time requirements of low-power robotic platforms. Additionally, most methods are designed for opaque objects and struggle with transparent objects due to the special properties of reflection and refraction. To address these challenges, we propose a Fast Depth Completion framework for Transparent objects (FDCT), which also benefits downstream tasks like object pose estimation. To leverage local information and avoid overfitting issues when integrating it with global information, we design a new fusion branch and shortcuts to exploit low-level features and a loss function to suppress overfitting. This results in an accurate and user-friendly depth rectification framework which can recover dense depth estimation from RGB-D images alone. Extensive experiments demonstrate that FDCT can run about 70 FPS with a higher accuracy than the state-of-the-art methods. We also demonstrate that FDCT can improve pose estimation in object grasping tasks. The source code is available at https://github.com/Nonmy/FDCT