Papers › DPOD: 6D Pose Object Detector and Refiner
DPOD: 6D Pose Object Detector and Refiner
Sergey Zakharov, Ivan Shugurov, Slobodan Ilic
In this paper we present a novel deep learning method for 3D object detection and 6D pose estimation from RGB images. Our method, named DPOD (Dense Pose Object Detector), estimates dense multi-class 2D-3D correspondence maps between an input image and available 3D models. Given the correspondences, a 6DoF pose is computed via PnP and RANSAC. An additional RGB pose refinement of the initial pose estimates is performed using a custom deep learning-based refinement scheme. Our results and comparison to a vast number of related works demonstrate that a large number of correspondences is beneficial for obtaining high-quality 6D poses both before and after refinement. Unlike other methods that mainly use real data for training and do not train on synthetic renderings, we perform evaluation on both synthetic and real training data demonstrating superior results before and after refinement when compared to all recent detectors. While being precise, the presented approach is still real-time capable.
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Code
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Code Syntology ran Syntology
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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 using RGB | LineMOD | DPOD | Accuracy (ADD) | 95.15% | #8 of 22 | Archive leaderboard | report |
| 6D Pose Estimation using RGB | LineMOD | DPOD | Mean ADD | 95.2 | #8 of 22 | Archive leaderboard | report |
| 6D Pose Estimation using RGB | Occlusion LineMOD | DPOD | Mean ADD | 47.25 | #9 of 13 | 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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