Papers › MaskedFusion: Mask-based 6D Object Pose Estimation
MaskedFusion: Mask-based 6D Object Pose Estimation
Nuno Pereira, Luís A. Alexandre
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).
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Code
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Tasks
Results from the paper archive 2025-07-28
| Task | Dataset | Model | Metric | Value | Rank at snapshot | Leaderboard | Report |
|---|---|---|---|---|---|---|---|
| 6D Pose Estimation | LineMOD | MaskedFusion | Accuracy (ADD) | 97.8 | #3 of 5 | Archive leaderboard | report |
| 6D Pose Estimation | YCB-Video | MaskedFusion | ADDS AUC | 93.3 | #8 of 10 | Archive leaderboard | report |
| 6D Pose Estimation using RGBD | LineMOD | MaskedFusion | Mean ADD | 97.8 | #4 of 8 | Archive leaderboard | report |
| 6D Pose Estimation using RGBD | YCB-Video | MaskedFusion | Mean ADD | 93.3 | #2 of 9 | Archive leaderboard | report |
| 6D Pose Estimation using RGBD | YCB-Video | MaskedFusion | Mean ADD-S | 93.3 | #2 of 9 | Archive leaderboard | report |
Ranks are positions in the archive's leaderboards as they stood at the 2025-07-28 snapshot. Results published since then are not among these rows, so a rank here is not a current standing.
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