Skip to content

Latest commit

 

History

History
152 lines (120 loc) · 5.66 KB

README.md

File metadata and controls

152 lines (120 loc) · 5.66 KB
Raw

Category-Level Articulated Object Pose Estimation(CVPR 2020)

Overview

This repository contains the implementation of ANSCH, a canonical representation for different articulated objects in a given category, together with an efficient neural network for pose estimation and joints regression of articulated objects from a single depth point cloud.

          

Paper  Project

Citing

If you find this code useful in your work, please consider citing:

@article{li2019category,
  title={Category-Level Articulated Object Pose Estimation},
  author={Li, Xiaolong and Wang, He and Yi, Li and Guibas, Leonidas and Abbott, A Lynn and Song, Shuran},
  journal={Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition},
  year={2020}
}

Content

Updates

  • [2024/08/08] We are trying to recover the checkpoints / dataset files due to a recent University-wide Google Drive reset. Meanwhile, feel free to check Hanxiao's ANCSH_Pytorch implementation for eyeglass and onedoor category.
  • ✔️ [2021/02/01] release the preprocessed synthetic dataset for 4 different categories, use rclone for Google Drive downloads

Setup

This implementation has been tested on Ubuntu 16.04 LTS, and CentOS 7.0, make sure CUDA and CUDNN are installed.

  • Two configurations have been tested:

    • Tensorfow 1.10, CUDA 9.0, and cudnn 7.1;
    • TensorFlow 1.12.0, CUDA 9.0 and cuDNN 7.4;

  • Clone the repository

git clone https://github.com/dragonlong/articulated-pose.git
  • Setup python environment
conda create -n articulated-pose python=3.6
source activate articulated-pose
pip install -r requirements.txt
sh compile_op.sh

Quick-Start

Online Codelab demos

You could check our online CodeLab demo to reproduce some of our results.

Dataset

You could simply download our pre-processed dataset to ./dataset folder. Below is a step-by-step tutorial on rendering your dataset using Pybullet, here we take eyeglasses in shape2motion for example:

  • Setup global path infos
  vim global_info.py
  • Download shape2motion dataset
bash download_shape2motion.sh
  • Create URDF
cd tools && python json2urdf.py
  • Rendering Data
python render_synthetic.py --dataset='shape2motion' --item='eyeglasses' --num=30 --cnt=31 --pitch="-90,5" --roll="-10,10" --yaw="-180,180" --min_angles="0,0" --max_angles="90,90"
  • Preprocessing
python preprocess_data.py --dataset='shape2motion' --item='eyeglasses'
  • Train/test split and visualization
cd ../lib && python dataset.py --item=eyeglasses --dataset=shape2motion --is_split --show_fig

Pretrained-Models

Please download our pretrained models from here(updated on April 22nd, 2020), and put into the ./results/model/ folder. Below are links to pretrained models of different categories, results might be slightly different with these checkpoints as we updated our training.

eyeglasses

oven

washing machine

laptop

drawer

Training

To train the network

python main.py --item='eyeglasses' --nocs_type='ancsh' --gpu='0'
python main.py --item='eyeglasses' --nocs_type='npcs' --gpu='1'

Evaluation

To generate estimation and evaluation results on per-part pose estimation, 3D iou, joint states, and joint parameters, you could simply run: bash evaluation.sh. Below is a detailed step by step tutorial:

1. Prediction

python main.py --item='eyeglasses' --nocs_type='ancsh' --test
python main.py --item='eyeglasses' --nocs_type='npcs' --test

2. Evaluation

2.1 post-processing

cd evaluation
python compute_gt_pose.py --item='eyeglasses' --domain='unseen' --nocs='ANCSH' --save

# npcs baseline estimation
python baseline_npcs.py --item='eyeglasses' --domain='unseen' --nocs='NPCS'

# run non-linear optimization over test group
python pose_multi_process.py --item='eyeglasses' --domain='unseen'

2.2 evaluation

# pose & relative
python eval_pose_err.py --item='eyeglasses' --domain='unseen' --nocs='ANCSH'

# 3d miou estimation
python compute_miou.py --item='eyeglasses' --domain='unseen' --nocs='ANCSH'

# evaluate joint estimations
python eval_joint_params.py --item='eyeglasses' --domain='unseen' --nocs='ANCSH'

Visualization

Please check the online demo for further visualizations.

Demo with point cloud rendering is inspired by: https://github.com/zekunhao1995/PointFlowRenderer.