This is the official source code for PVN3D: A Deep Point-wise 3D Keypoints Voting Network for 6DoF Pose Estimation, CVPR 2020. (PDF, Video_bilibili, Video_youtube).

We optimized and applied PVN3D for a robotic manipulation contest OCRTOC (IROS 2020: Open Cloud Robot Table Organization Challenge) and got 2nd place! The model was trained on synthesis data generated from rendering engine and only a few frames (about 100 frames) of real data but generalize to real scenes during inference, revealing its capability of cross-domain generalization.

• The following setting is for pytorch 1.0.1. For pytorch 1.5 & cuda 10, switch to branch pytorch-1.5.
• Install CUDA9.0
• Set up python environment from requirement.txt:

  pip3 install -r requirement.txt 

• Install tkinter through sudo apt install python3-tk
• Install python-pcl.
• Install PointNet++ (refer from Pointnet2_PyTorch):

  python3 setup.py build_ext

• LineMOD: Download the preprocessed LineMOD dataset from here (refer from DenseFusion). Unzip it and link the unzipped Linemod_preprocessed/ to pvn3d/datasets/linemod/Linemod_preprocessed:

  ln -s path_to_unzipped_Linemod_preprocessed pvn3d/dataset/linemod/

• YCB-Video: Download the YCB-Video Dataset from PoseCNN. Unzip it and link the unzippedYCB_Video_Dataset to pvn3d/datasets/ycb/YCB_Video_Dataset:

  ln -s path_to_unzipped_YCB_Video_Dataset pvn3d/datasets/ycb/

• First, generate synthesis data for each object using scripts from raster triangle.
• Train the model for the target object. Take object ape for example:

  cd pvn3d
  python3 -m train.train_linemod_pvn3d --cls ape

  The trained checkpoints are stored in train_log/linemod/checkpoints/{cls}/, train_log/linemod/checkpoints/ape/ in this example.

• Start evaluation by:

  # commands in eval_linemod.sh
  cls='ape'
  tst_mdl=train_log/linemod/checkpoints/${cls}/${cls}_pvn3d_best.pth.tar
  python3 -m train.train_linemod_pvn3d -checkpoint $tst_mdl -eval_net --test --cls $cls

  You can evaluate different checkpoint by revising tst_mdl to the path of your target model.
• We provide our pre-trained models for each object at onedrive link, baiduyun link (access code(提取码): 8kmp). Download them and move them to their according folders. For example, move the ape_pvn3d_best.pth.tar to train_log/linemod/checkpoints/ape/. Then revise tst_mdl=train_log/linemod/checkpoints/ape/ape_pvn3d_best.path.tar for testing.

• After training your models or downloading the pre-trained models, you can start the demo by:

  # commands in demo_linemod.sh
  cls='ape'
  tst_mdl=train_log/linemod/checkpoints/${cls}/${cls}_pvn3d_best.pth.tar
  python3 -m demo -dataset linemod -checkpoint $tst_mdl -cls $cls

  The visualization results will be stored in train_log/linemod/eval_results/{cls}/pose_vis

• Preprocess the validation set to speed up training:

  cd pvn3d
  python3 -m datasets.ycb.preprocess_testset

• Start training on the YCB-Video Dataset by:

  python3 -m train.train_ycb_pvn3d

  The trained model checkpoints are stored in train_log/ycb/checkpoints/

• Start evaluating by:

  # commands in eval_ycb.sh
  tst_mdl=train_log/ycb/checkpoints/pvn3d_best.pth.tar
  python3 -m train.train_ycb_pvn3d -checkpoint $tst_mdl -eval_net --test

  You can evaluate different checkpoint by revising the tst_mdl to the path of your target model.
• We provide our pre-trained models at onedrive link, baiduyun link (access code(提取码): h2i5). Download the ycb pre-trained model, move it to train_log/ycb/checkpoints/ and modify tst_mdl for testing.

• After training your model or downloading the pre-trained model, you can start the demo by:

  # commands in demo_ycb.sh
  tst_mdl=train_log/ycb/checkpoints/pvn3d_best.pth.tar
  python3 -m demo -checkpoint $tst_mdl -dataset ycb

  The visualization results will be stored in train_log/ycb/eval_results/pose_vis

• Evaluation result on the LineMOD dataset:
• Evaluation result on the YCB-Video dataset:
• Visualization of some predicted poses on YCB-Video dataset:
• Joint training for distinguishing objects with similar appearance but different in size:

• Compile the FPS scripts (refer from clean-pvnet):

  cd lib/utils/dataset_tools/fps/
  python3 setup.py build_ext --inplace

• Generate information of objects, eg. radius, 3D keypoints, etc. in your datasets with the gen_obj_info.py script:

  cd ../
  python3 gen_obj_info.py --help

• Modify info of your new dataset in PVN3D/pvn3d/common.py
• Write your dataset preprocess script following PVN3D/pvn3d/datasets/ycb/ycb_dataset.py (for multi objects of a scene) or PVN3D/pvn3d/datasets/linemod/linemod_dataset.py (for single object of a scene). Note that you should modify or call the function that get your model info, such as 3D keypoints, center points, and radius properly.
• (Important!) Visualize and check if you process the data properly, eg, the projected keypoint and center point, the semantic label of each point, etc.
• For inference, make sure that you load the 3D keypoints, center point, and radius of your objects in the object coordinate system properly in PVN3D/pvn3d/lib/utils/pvn3d_eval_utils.py.

Please cite PVN3D if you use this repository in your publications:

@InProceedings{He_2020_CVPR,
author = {He, Yisheng and Sun, Wei and Huang, Haibin and Liu, Jianran and Fan, Haoqiang and Sun, Jian},
title = {PVN3D: A Deep Point-Wise 3D Keypoints Voting Network for 6DoF Pose Estimation},
booktitle = {IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
month = {June},
year = {2020}
}