Submission name | icpv4 | ||||||||||
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Submission time (UTC) | Aug. 19, 2020, 9:33 p.m. | ||||||||||
User | wangg16 | ||||||||||
Task | Pose estimation (BOP 2019-2022) | ||||||||||
Dataset | LM-O | ||||||||||
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 | wangg16 |
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Publication | Li et al.: CDPN: Coordinates-Based Disentangled Pose Network for Real-Time RGB-Based 6-DoF Object Pose Estimation, ICCV 2019 |
Implementation | https://github.com/LZGMatrix/BOP19_CDPN_2019ICCV/tree/bop2020 |
Training image modalities | RGB |
Test image modalities | RGB-D |
Description | 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 [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 includes:
[1] https://github.com/aim-uofa/AdelaiDet/tree/master/configs/FCOS-Detection/vovnet [2] https://github.com/DLR-RM/AugmentedAutoencoder |
Computer specifications | Intel i7-7700; GPU: GTX 1070; Memory: 16G |