News

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

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).

The trained CDPNv2 weights for BOP20 Challenge have been provided!

Our test environments (the same as CDPN in bop19)

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

Detection

Please refer to detection/README.md

Pose Estimation

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.

Data Preparation

Download the 7 core datasets from the BOP website
Download our trained models(code: 3jfn) and detection results.
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

Run

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'

Use the Bop_toolkit for evaluation.

ImYuxuanLi / CDPN-pytorch