Method: TF6D (Default, CNOS) + Megapose (5 hypothesis)

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

Public submissions

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