| User | tf6d |
|---|---|
| Publication | |
| Implementation | |
| Views | single |
| Test image modalities | RGB |
| Description | Submitted to: BOP Challenge 2024 Training data: None, our method does not require any task-specific training Onboarding data: Model-based rendering, 40 rendered templates Used 3D models: Default, CAD Notes: 1) Testing data: RGB 2) Segmentation method: Default, CNOS 3) Our method does not introduce any additional or task-specific training 4) Pose refinement: Yes, the pose refinement network is from MegaPose with 5 hypotheses 5) Key idea: Our method leverages 3D-consistent features derived from 3D foundation models for training-free 6D pose estimation, rather than relying on 2D foundation models (e.g., DINO). |
| Computer specifications | NVIDIA A100 80GB PCIe + Intel(R) Xeon(R) Gold 6342 CPU @ 2.80GHz |
| Date | Submission name | Dataset | ||
|---|---|---|---|---|
| 2024-11-13 15:13 | TF6D (CNOS) + Megapose (5 hypothesis) | HB | ||
| 2024-11-14 04:23 | TF6D (CNOS) + Megapose (5 hypothesis) | ITODD | ||
| 2024-11-29 12:40 | TF6D (CNOS) + Megapose (5 hypothesis) | LM-O | ||
| 2024-11-29 12:41 | TF6D (CNOS) + Megapose (5 hypothesis) | TUD-L | ||
| 2024-11-29 12:42 | TF6D (CNOS) + Megapose (5 hypothesis) | IC-BIN | ||
| 2024-11-29 12:43 | TF6D (CNOS) + Megapose (5 hypothesis) | YCB-V | ||
| 2024-11-29 13:00 | TF6D (CNOS) + Megapose (5 hypothesis) | T-LESS |