|Submission time (UTC)||Aug. 19, 2020, 9:33 p.m.|
|Task||Pose estimation (BOP 2019-2022)|
|Training model type||Default|
|Training image type||Synthetic (only PBR images provided for BOP Challenge 2020 were used)|
|Publication||Li et al.: CDPN: Coordinates-Based Disentangled Pose Network for Real-Time RGB-Based 6-DoF Object Pose Estimation, ICCV 2019|
|Training image modalities||RGB|
|Test image modalities||RGB-D|
In PBR-only setting, all models are trained only using the provided PBR synthetic data and tested with depth/ICP refinement. For each dataset, we trained a CDPN model for each object.
For detection, different from CDPN in BOP19, we used the FCOS with BackBone of vovnet-V2-57-FPN . We trained a detector for each dataset. The detector was trained for 8 epochs with batch size of 4 on a single GPU, 4 workers, and a learning rate of 1e-3. We used color augmentation similar to AAE  during training.
For pose estimation, the difference between our CDPNv2 and the BOP19-version CDPN mainly includes:
|Computer specifications||Intel i7-7700; GPU: GTX 1070; Memory: 16G|