OPDPN: Baseline in "OPD: Single-view 3D Openable Part Detection"

Overview

This repository contains the implementation of OPDPN, a simple baseline that detects openable parts and predicts their motion parameters from a single-view point cloud.

Paper  Website

Content

• Additional Repo
• Setup
• Dataset
• Pretrained Models
• Training
• Inference
• Evaluation & Visualization

Additional-Repo

• OPD: We implement the OPDRCNN (main methods in this paper). For details, check the OPD repo.

• ANCSH: We reimplement the ANCSH method using PyTorch for the paper "Category-Level Articulated Object Pose Estimation". For details, check the ANCSH-pytorch repo.

Setup

The implementation has been tested on Ubuntu 20.04, with PyTorch 1.7.1, CUDA 11 and CUDNN 8.0.3.

• Clone the repository

git clone git@github.com:3dlg-hcvc/OPDPN.git

• Setup python environment

conda create -n opdpn python=3.7 
conda activate opdpn  
pip install -r requirements.txt
pip install "git+git://github.com/erikwijmans/Pointnet2_PyTorch.git#egg=pointnet2_ops&subdirectory=pointnet2_ops_lib"

(If meeting unauthenticated git protocol on port 9418 ), run git config --global url."https://".insteadOf git:// for above last command to install pointnet2_ops_lib)

Dataset

You can download our [OPDSynth](dataset) and [OPDReal](dataset_real) datasets for OPDPN to ./dataset folder (there is one dataset.tar.gz under /dataset/OPDPN).

Pretrained-Models

You can download our pretrained models to ./models folder (there is one models.tar.gz under /models/OPDPN).

Models for OPDSynth start with Synth, for OPDReal start with Real

[OPDPN]

Training

To train from the scratch, you can use below commands.

• python train.py \
  --train_path <PATH_TO_TRAIN> \
  --test_path <PATH_TO_TEST> \
  --output_dir train_output

  • Dataset:
    • OPDSynth:
      • --train-path dataset/dataset/train.h5
      • --test-path dataset/dataset/val.h5
    • OPDReal:
      • --train-path dataset/dataset_real/train.h5
      • --test-path dataset/dataset_real/val.h5

Inference

Inference with pretrained model, or your own trained model on val set

python train.py \
--train_path <PATH_TO_TRAIN> \
--test_path <PATH_TO_TEST> \
--output_dir test_output \
--test \
--inference_model <PPRETRAINED_MODEL>

• Dataset needs the same options as above

Evaluation&Visualization

We convert the results into OPD inference results format, evalaute and visualize using the code from OPD

• Finalize the results

  • Synth Data

    python finalize_result.py \
    --result_path <PATH_TO_RESULT> \
    --output test_output
    

  • Real Data

    python finalize_result_real.py \
    --result_path <PATH_TO_RESULT> \
    --output test_output
    

  The <PATH_TO_RESULT> is the inference_result.h5 after running inference.

• Get the final_result.h5, and conver the results into OPDRCNN format (Use the code from OPD). Need to download the dataset for OPDRCNN in OPD.)

  python <PATH_TO_OPD>/PC_convert_results.py \
  --result-path <PATH_TO_FINAL_RESULT> \
  --data-path <PATH_TO_DATASET>
  

  • Dataset:
    • Synth Data:
      • --data-path <PATH_TO_OPD>/dataset/MotionDataset_h5_6.11
    • Real Data:
      • --data-path <PATH_TO_OPD>/dataset/MotionDataset_h5_real

• After converting, get the inference file in the OPD format, check Evaluation in README of OPD. (Use --inference-file option)