• We have released the official training code of CDPN to reproduce the impressive results in our ICCV2019 paper.
• Find CDPN for BOP19 in the (https://github.com/LZGMatrix/BOP19_CDPN_2019ICCV)
• Find CDPN trained with PBR data for BOP20 (https://github.com/LZGMatrix/BOP19_CDPN_2019ICCV/tree/bop2019_pbr)

CDPNv2 for BOP20 challenge (mainly based on "CDPN: Coordinates-Based Disentangled Pose Network for Real-Time RGB-Based 6-DoF Object Pose Estimation" on ICCV 2019).

• Ubuntu 16.04 (64bit)
• Python 3.6.7
• Pytorch 0.4.1
• CUDA 9.0
• Bop_toolkit
• numpy, cv2, plyfile, tqdm, scipy, progress, etc.

• Please refer to detection/README.md

The difference between our CDPNv2 and the BOP19-version CDPN mainly including:

• Domain Randomization: We used stronger domain randomization operations than BOP19. The details will be provided after the deadline.

• 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. Download the 7 core datasets from the BOP website

2. Download our trained models(code: 3jfn) and detection results.

3. Prepare the data as follows:

   Note:

   • models_eval: downloaded official models;
   • test/test_primesense: downloaded official BOP19 test set;
   • val:optionally, downloaded official val set;
   • trained_models: our provided trained models;
   • bbox_retinanet: our provided detection results;
   • exp: save the test result files

Root
├── dataset
│   ├── lmo_bop19
│   │   ├── models_eval 
│   │   └── test 
│   ├── tudl_bop19
│   │   ├── models_eval 
│   │   └── test 
│   ├── hb_bop19
│   │   ├── models_eval
│   │   ├── val 
│   │   └── test
│   ├── icbin_bop19
│   │   ├── models_eval
│   │   └── test 
│   ├── itodd_bop19
│   │   ├── models_eval 
│   │   ├── val
│   │   └── test
│   ├── tless_bop19
│   │   ├── models_eval
│   │   └── test_primesense 
│   └── ycbv_bop19
│       ├── models_eval 
│       └── test
├── trained_models
│   ├── lmo
│   │   ├── obj_ape.checkpoint
│   │   └── ...
│   └── ...
├── bbox
│   ├── lmo
│   │   ├── lmo_test_bop19_000002.json
│   │   └── ... 
│   └── ...
├── lib
├── tools
└── exp

1. In 'tools' directory, run

  sh run.sh

It will first generate a .csv file to record the result of each object for each dataset. The final result files can be found in 'exp/final_result/CDPN_xxxx-test.csv'

2. Use the Bop_toolkit for evaluation.