Submission name | Zhigang-CDPNv2 (MODE 2, FCOS) | ||||||||||
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Submission time (UTC) | Aug. 19, 2020, 12:19 p.m. | ||||||||||
User | ZhigangLi | ||||||||||
Task | Model-based 6D localization of seen objects | ||||||||||
Dataset | IC-BIN | ||||||||||
Training model type | Default | ||||||||||
Training image type | Synthetic (only PBR images provided for BOP Challenge 2020 were used) | ||||||||||
Description | |||||||||||
Evaluation scores |
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User | ZhigangLi |
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Publication | CDPN: Coordinates-Based Disentangled Pose Network for Real-Time RGB-Based 6-DoF Object Pose Estimation |
Implementation | https://github.com/LZGMatrix/BOP19_CDPN_2019ICCV/tree/bop2020 |
Training image modalities | RGB |
Test image modalities | RGB |
Description | 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:
Besides the color augmentation similar to AAE [2], we also used the truncation domain randomization in [3] to improve the system robustness to occlusion.
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.
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
[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 |
Computer specifications | Intel i7-7700; GPU: GTX 1070; Memory: 16G |