| Submission name | cir | ||||||||||
|---|---|---|---|---|---|---|---|---|---|---|---|
| Submission time (UTC) | Jan. 28, 2024, 6:26 p.m. | ||||||||||
| User | r10922190 | ||||||||||
| Task | Model-based 6D localization of seen objects | ||||||||||
| Dataset | IC-BIN | ||||||||||
| Description | |||||||||||
| Evaluation scores |
|
| User | r10922190 |
|---|---|
| Publication | Hong, Zong-Wei and Hung, Yen-Yang and Chen, Chu-Song: RDPN6D: Residual-based Dense Point-wise Network for 6Dof Object Pose Estimation Based on RGB-D Images, CVPRW 2024 |
| Implementation | https://github.com/AI-Application-and-Integration-Lab/RDPN6D |
| Training image modalities | RGB-D |
| Test image modalities | RGB-D |
| Description | Submitted to: BOP Challenge 2024 Training data: real + provided PBR Used 3D models: Reconstructed for T-LESS, default for other datasets Notes: Authors: Hong, Zong-Wei and Hung, Yen-Yang and Chen, Chu-Song (National Taiwan University) Following GDRNPP, the detector is using YOLOX trained on real + PBR datasets and we also utilize depth information (CIR) to further refine the estimated pose. A network is trained for each object. |
| Computer specifications | RTX3090 |