Papers › DeepIM: Deep Iterative Matching for 6D Pose Estimation
DeepIM: Deep Iterative Matching for 6D Pose Estimation
Yi Li, Gu Wang, Xiangyang Ji, Yu Xiang, Dieter Fox
Estimating the 6D pose of objects from images is an important problem in various applications such as robot manipulation and virtual reality. While direct regression of images to object poses has limited accuracy, matching rendered images of an object against the observed image can produce accurate results. In this work, we propose a novel deep neural network for 6D pose matching named DeepIM. Given an initial pose estimation, our network is able to iteratively refine the pose by matching the rendered image against the observed image. The network is trained to predict a relative pose transformation using an untangled representation of 3D location and 3D orientation and an iterative training process. Experiments on two commonly used benchmarks for 6D pose estimation demonstrate that DeepIM achieves large improvements over state-of-the-art methods. We furthermore show that DeepIM is able to match previously unseen objects.
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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 | PoseCNN + DeepIM | Accuracy | 97.5 | #12 of 22 | Archive leaderboard | report |
| 6D Pose Estimation using RGB | LineMOD | PoseCNN + DeepIM | Accuracy (ADD) | 88.1% | #12 of 22 | Archive leaderboard | report |
| 6D Pose Estimation using RGB | LineMOD | PoseCNN + DeepIM | Mean ADD | 88.6 | #12 of 22 | Archive leaderboard | report |
| 6D Pose Estimation using RGB | Occlusion LineMOD | DeepIM (Train on Occlusion LineMOD) | Mean ADD | 55.5 | #4 of 13 | Archive leaderboard | report |
| 6D Pose Estimation using RGBD | YCB-Video | PoseCNN + DeepIM | Mean ADD | 80.6 | #3 of 9 | Archive leaderboard | report |
| 6D Pose Estimation using RGBD | YCB-Video | PoseCNN + DeepIM | Mean ADI | 92.4 | #3 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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