Please refer to our paper for detailed explanation. Arxiv Link is here. In the following, ROOT refers to the folder containing this README file.

Before you running this repository, please look at this one: https://github.com/sjtuytc/segmentation-driven-pose. It's much greater because it's:

1. RGB only.
2. SOTA precision and speed.
3. Multi Object.
4. Training end-to-end instead of two-step.
5. Code super easy to understand and fully open-source.

1. All the codes are tested in Python, CUDA 8.0 and CUDNN 5.1.
2. Install pytorch 0.4.0 and other dependencies.
3. Download LineMod dataset here. Only folders called models and test are needed. Put them in DATAROOT/models and DATAROOT/test where DATAROOT can be any folder you'd like to place LineMod dataset.

You can skip this step since we have provided designated keypoints files in '$ROOT/1_keypoint_designator/assets/sifts/'.

1. The related code is in $ROOT/1_keypoint_designator/.

   $ cd ROOT/1_keypoint_designator/

2. Place the input model file (e.g. DATAROOT/models/obj_01.ply) in $./assets/models/
3. Build the code and run it. Just type:

   $ sh build_and_run.sh

   The output file is in $./assets/sifts/. It's a ply file storing the 3D coordinates of designated keypoints.

1. The related code is in $ROOT/2_keypoint_annotator/.

   $ cd ROOT/2_keypoint_annotator/

2. Run keypoint annotator on one object of LineMod.

   $ python annotate_keypoint.py --obj_id 1 --total_kp_number 50 --output_base ROOT/3_6Dpose_estimator/data --sixd_base DATAROOT

   Type the following to see the meaning of options.

   $ python annotate_keypoint.py -h

3. The annotated keypoints are in file annot_train.h5 and annot_eval.h5. The corresponding training images are in folders train and eval.

1. Relative files locate in $ROOT/3_6Dpose_estimator/train_YOLO.

   $ cd ROOT/3_6Dpose_estimator/train_YOLO

2. Build Darknet (YOLOv3).

   $ make

3. Prepare data as AlexeyAB/darknet's instructions. Refer to folder ./scripts for more help.
4. Download pretrained darknet53 here
5. Run train_single.sh or train_all.sh to train the network.
6. Put trained weights (e.g. 01.weights) in folder $ROOT/3_6Dpose_estimator/models/yolo/.

1. Relative code is in $ROOT/3_6Dpose_estimator/train_KPD/

   $ cd ROOT/3_6Dpose_estimator/train_KPD

2. Modify Line 19, 21, 39, 46 of file./src/utils/dataset/coco.py to previously annotated dataset. Examples are given in these lines.
3. Train on Linemod dataset without DPG.

   $ python src/train.py --trainBatch 28 --expID seq5_Nov_1_1 --optMethod adam

4. Train on Linemod dataset with DPG. Just add a --addDPG option. and load the model trained after in the second step.

   $ python src/train.py --trainBatch 28 --expID seq5_dpg_Nov_1_1 --optMethod adam --loadModel ./exp/coco/seq5_Nov_1_1/model_100.pkl --addDPG

5. (Optional) Visualize training process. Type

   $ tensorboard --logdir ./

1. Move back to the root of pose estimator.

   $ cd ROOT/3_6Dpose_estimator/

2. Run the following command.

   $ CUDA_VISIBLE_DEVICES=1 python3 betapose_evaluate.py --nClasses 50 --indir /01/eval --outdir examples/seq1 --sp --profile

   The output json file containing predicted 6D poses will be in examples/seq1.