| Submission name | w real images | ||||||||||||||
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| Submission time (UTC) | March 9, 2026, 1:01 p.m. | ||||||||||||||
| User | NUDTCJH | ||||||||||||||
| Task | Model-based 6D detection of seen objects | ||||||||||||||
| Dataset | XYZ-IBD | ||||||||||||||
| Description | we use train_pbr and real images for training. 2D detection: YOLO11x | ||||||||||||||
| Evaluation scores |
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| User | NUDTCJH |
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| Publication | |
| Implementation | |
| Training image modalities | RGB |
| Test image modalities | RGB |
| Description | We first predict dense keypoints through a single view, and use these keypoints to match instances across multiple views. We then further refine the keypoints and reconstruct the 3D point cloud through multi-view feature-level fusion of these matched instances. The final pose is solved using the Umeyama algorithm. |
| Computer specifications | 5090 |