User | epi |
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Publication | |
Implementation | |
Training image modalities | RGB |
Test image modalities | RGB |
Description | The presented results were achieved by the refinement-free version of FoundPose (row 1 of Table 1 in [A]). In this submission, FoundPose uses default CNOS-FastSAM [B] segmentations provided for BOP'23. For pose estimation, the method uses features from layer 18 of DINOv2 (ViT-L) with registers [C]. Note that FoundPose doesn't do any task-specific training -- it only uses frozen FastSAM (via CNOS) and frozen DINOv2. [A] Anonymous: FoundPose: Unseen Object Pose Estimation with Foundation Features. [B] Nguyen et al.: CNOS: A Strong Baseline for CAD-based Novel Object Segmentation, ICCVW 2023. [C] Darcet et al.: Vision transformers need registers, arXiv 2023. |
Computer specifications | Tesla P100 16GB |
Date | Submission name | Dataset | ||
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2023-11-15 18:05 | FoundPose-Coarse | ITODD | ||
2023-11-15 18:14 | FoundPose-Coarse | HB | ||
2024-01-27 11:23 | FoundPose-Coarse | LM-O | ||
2024-01-27 11:33 | FoundPose-Coarse | IC-BIN | ||
2024-01-28 19:18 | FoundPose-Coarse | YCB-V | ||
2024-01-28 19:19 | FoundPose-Coarse | T-LESS | ||
2024-01-28 19:19 | FoundPose-Coarse | TUD-L |