Submission name | FoundPose-Coarse | ||||||||||
---|---|---|---|---|---|---|---|---|---|---|---|
Submission time (UTC) | Nov. 15, 2023, 6:14 p.m. | ||||||||||
User | epi | ||||||||||
Task | Model-based 6D localization of unseen objects | ||||||||||
Dataset | HB | ||||||||||
Training model type | Default | ||||||||||
Training image type | None | ||||||||||
Description | |||||||||||
Evaluation scores |
|
User | epi |
---|---|
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 |