Awesome

News

We have released the official training code of CDPN to reproduce the impressive results in our ICCV2019 paper.
Find CDPNv2 for BOP20 in the branch of bop2020
Find CDPN trained with PBR data for BOP20 in the branch of bop2019_pbr

BOP19_CDPN_2019ICCV

The modified version of CDPN ("CDPN: Coordinates-Based Disentangled Pose Network for Real-Time RGB-Based 6-DoF Object Pose Estimation" on ICCV 2019) for BOP: Benchmark for 6D Object Pose Estimation Challenge 2019.

Our test environments

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

For detection, we trained a RetinaNet for each dataset on mmdetection.

Please refer to DETECTION.md

Pose Estimation

In the BOP 2019 challenge, different from the paper, both of the rotation and translation are solved from the built 2D-3D correspondences by PnP algorithm. We trained a CDPN model for each object.

Data Preparation

Download the 7 core datasets from the BOP website
Download our trained models 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_retinanet
│   ├── lmo
│   │   ├── lmo_test_bop19_000002.json
│   │   └── ... 
│   └── ...
├── lib
├── tools
├── detection
└── 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.