Comments (1)
To be clear, there is no training in our framework. During the onboarding and proposal stage, we use the pretrained model provided by SAM (or FastSAM) and DINOv2.
For SAM (or FastSAM), we use its default not prompt setting by sampling 64 points per dimension. There is still a problem of over-segmentation but it can be filtered in the matching stage since their over-segmentation masks have lower confidence score comparing to correct segmentation masks. For example, we show in the teaser the segmentation on YCB-V dataset and most of the over-segmentation masks are filtered out.
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Related Issues (20)
- Any standard for scaling CAD models? HOT 2
- Problems with detecting multi-color objects (?) HOT 1
- How to use this project? What are parameters of train ? HOT 2
- precomputed results on YCBV HOT 2
- Pixel to point correspondances with renderer HOT 1
- Installation of ultralytics HOT 1
- Segmentation evaluation using bop_toolkit HOT 2
- CAD-object free results HOT 3
- Why is the paper's reported result different from that in the BOP leaderboard? HOT 3
- size mismatch for pos_embed HOT 3
- GPU misconfiguration HOT 1
- Using FastSAM on custom dataset inference HOT 6
- Poor performance in a object with two colors.
- release pre-computed Linemod and YCBV results HOT 1
- Why are the results in the paper different from those in the baseline of the BOP? HOT 1
- pre-computed segmentation is not complete on the YCBV dataset HOT 2
- Running custom inference on multiple GPUs HOT 2
- Rendering templates HOT 1
- DINOv2 for image feature extraction HOT 2
- Limited performance on Custom Datasets HOT 8
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