# MaskedFusion: Mask-based 6D Object Pose Estimation

arXiv: https://arxiv.org/abs/1911.07771 · HTML: https://mlsanity.com/paper/1911.07771/

## Key facts

- Cited by 9 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 18 Nov 2019.

## Built on by (most cited first, 9 of 9)

- [StereOBJ-1M: Large-scale Stereo Image Dataset for 6D Object Pose Estimation](https://mlsanity.com/paper/2109.10115/) (2021)
- [Uni6D: A Unified CNN Framework without Projection Breakdown for 6D Pose Estimation](https://mlsanity.com/paper/2203.14531/) (2022)
- [Deep Fusion Transformer Network with Weighted Vector-Wise Keypoints Voting for Robust 6D Object Pose Estimation](https://mlsanity.com/paper/2308.05438/) (2023)
- [RDPN6D: Residual-based Dense Point-wise Network for 6Dof Object Pose Estimation Based on RGB-D Images](https://mlsanity.com/paper/2405.08483/) (2024)
- [Towards Deep Learning-based 6D Bin Pose Estimation in 3D Scans](https://mlsanity.com/paper/2112.09598/) (2021)
- [Generating Annotated Training Data for 6D Object Pose Estimation in Operational Environments with Minimal User Interaction](https://mlsanity.com/paper/2103.09696/) (2021)
- [MPF6D: Masked Pyramid Fusion 6D Pose Estimation](https://mlsanity.com/paper/2111.09378/) (2021)
- [FastPoseCNN: Real-Time Monocular Category-Level Pose and Size Estimation Framework](https://mlsanity.com/paper/2406.11063/) (2024)
- [DLTPose: 6DoF Pose Estimation From Accurate Dense Surface Point Estimates](https://mlsanity.com/paper/2504.07335/) (2025)

## Abstract (arXiv)

MaskedFusion is a framework to estimate the 6D pose of objects using RGB-D data, with an architecture that leverages multiple sub-tasks in a pipeline to achieve accurate 6D poses. 6D pose estimation is an open challenge due to complex world objects and many possible problems when capturing data from the real world, e.g., occlusions, truncations, and noise in the data. Achieving accurate 6D poses will improve results in other open problems like robot grasping or positioning objects in augmented reality. MaskedFusion improves the state-of-the-art by using object masks to eliminate non-relevant data. With the inclusion of the masks on the neural network that estimates the 6D pose of an object we also have features that represent the object shape. MaskedFusion is a modular pipeline where each sub-task can have different methods that achieve the objective. MaskedFusion achieved 97.3% on average using the ADD metric on the LineMOD dataset and 93.3% using the ADD-S AUC metric on YCB-Video Dataset, which is an improvement, compared to the state-of-the-art methods. The code is available on GitHub (https://github.com/kroglice/MaskedFusion).