In PBR-only setting, all models are trained only using the provided PBR synthetic data. 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 [1]. 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 [2] during training.

For pose estimation, the difference between our CDPNv2 and the BOP19-version CDPN mainly including:

• Domain Randomization

Besides the color augmentation similar to AAE [2], we also used the truncation domain randomization in [3] to improve the system robustness to occlusion.

• Network Architecture

Considering the organizer provides high-quality PBR synthetic training data in BOP20, we adopt a deeper 34-layer Resnet as the backbone instead of the 18-layer Resnet used in BOP19-version CDPN. Also, the fancy concat structures in BOP19-version CDPN are removed. The input and output resolutions are 256×256 and 64×64 respectively.

• Training

During training, the initial learning rate was 1 × 10−4 and the batch size was 6. We used RMSProp with alpha 0.99 and epsilon 1× 10−8 to optimize the network. The model was trained for 160 epochs in total and the learning rate was divided by 10 every 50 epochs

• Other implementations, such as the coordinates labels were computed by back-projection from the rendered depth, instead of forward-projection with z-buffer.

[1] https://github.com/aim-uofa/AdelaiDet/tree/master/configs/FCOS-Detection/vovnet

[2] https://github.com/DLR-RM/AugmentedAutoencoder

[3] https://arxiv.org/abs/2008.08391