This repo is a recreation of the methodology for 6D pose estimation by DPOD: 6D Pose Object Detector and Refiner ,written by Sergey Zakharov, Ivan Shugurov, Slobodan Ilic [1]. Given a RGB image, the goal is to estimate the 6D pose of an object. Model was trained and tested entirely on the Linemod dataset [2]. For every 99 out of 100 images, MSCoco dataset [3] was used to change the background of the input image for the correspondence block to prevent overfitting.

Note: Green box represents the ground truth pose and red box represents predicted pose

$ git clone https://github.com/yshah43/DPOD.git
$ cd DPOD
$ chmod +x dataset_install.sh
$ ./dataset_install.sh
$ pip install -r requirements.txt
$ python train.py
$ python eval.py

Below is a brief description of the .py files:

1. create_ground_truth.py - create the grund truth ID, U and V masks and the changed background image.
2. correspondence_block.py - trainining loop for the correspondence block base on the Unet encoder decoder architecture
3. pose_block.py - use PnP and RANSAC for initial pose estimation
4. create_renderings.py - create input for DL based pose refiner
5. pose_refinement.py - training loop for DL based pose refiner
6. train.py - trainining script for all of the above
7. eval.py - evaluation script for all of the above

• Add additional in-plane rotations to artificially simulate new poses while training
• Add resnet18 as the encoder decoder architecture as described in section A.1
• Train and evaluate the model on Occlusion Dataset and with synthetic data

[1] Sergey Zakharov, Ivan Shugurov, and Slobodan Ilic. Dpod: 6d pose object detector and refiner. In Proceedings of the IEEE International Conference on Computer Vision, pages 1941–1950, 2019.

[2] Hinterstoisser et al. "Model based training, detection and pose estimation of texture-less 3d objects in heavily cluttered scenes" ACCV 2012.

[3] Tsung-Yi Lin, Michael Maire, Serge Belongie, James Hays, Pietro Perona, Deva Ramanan, Piotr Dolla ́r, and C Lawrence Zitnick. Microsoft coco: Common objects in context. In European conference on computer vision, pages 740–755. Springer, 2014.