josslei / opd

Some modification to [ECCV 2022, Oral] OPD: Single-view 3D Openable Part Detection

OPD: Single-view 3D Openable Part Detection [ECCV 2022, Oral]

Hanxiao Jiang, Yongsen Mao, Manolis Savva, Angel Xuan Chang

Overview

This repository contains the implementation of OPDRCNN, a neural architecture that detects openable parts and predicts their motion parameters from a single-view image. The code is developed based on Detectron2.

Paper  Website

Content

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

Additional-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.

• OPDPN: We implement the OPDPN baseline (proposed in this paper). For details, check the OPDPN 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 [email protected]:3dlg-hcvc/OPD.git

• Setup python environment

conda create -n opd python=3.7 
conda activate opd  
pip install -r requirements.txt

Dataset

You can download our [OPDSynth] (MotionDataset_h5_6.11) and [OPDReal] (MotionDataset_h5_real) datasets to ./dataset folder (there is one dataset.tar.gz under /dataset/OPD).

Pretrained-Models

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

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

[OPDRCNN-O RGB]     [OPDRCNN-O Depth]     [OPDRCNN-O RGBD]

[OPDRCNN-C RGB]     [OPDRCNN-C Depth]     [OPDRCNN-C RGBD]

Training

To train from the scratch, you can use below commands. The output will include evaluation results on the val set.

• Train the only_det model (only train the detection and segmentation) -> only det model has no difference for OPDRCNN-O or -C.

  python train.py \
  --config-file configs/bmcc.yaml \
  --output-dir train_output \
  --data-path <PATH_TO_DATASET> \
  --input-format <RGB/depth/RGBD> \
  --model_attr_path <PATH_TO_ATTR> \
  --only_det \
  --opts SOLVER.BASE_LR 0.005 SOLVER.MAX_ITER 30000 SOLVER.STEPS '(18000.0, 24000.0)' SOLVER.CHECKPOINT_PERIOD 5000 

  • Dataset:

    • OPDSynth:

      • --data-path dataset/MotionDataset_h5_6.11
      • --model_attr_path dataset/MotionDataset_h5_6.11/urdf-attr.json
• Pick the best only detections model for different inputs RGB/depth/RGBD
• Continue training the full models with the best only detection models

  python train.py \
  --config-file <MODEL_CONFIG> \
  --output-dir train_output \
  --data-path <PATH_TO_DATASET> \
  --input-format <RGB/depth/RGBD> \
  --model_attr_path <PATH_TO_ATTR> \
  --extrinsic_weight 15 \
  --motion_weights 1 8 8 \
  --opts MODEL.WEIGHTS <PPRETRAINED_MODEL> SOLVER.BASE_LR 0.001 SOLVER.MAX_ITER 60000 SOLVER.STEPS '(36000, 48000)' SOLVER.CHECKPOINT_PERIOD 5000

  • Model:

    • OPDRCNN-O:
      • --config-file configs/bmoc_v0.yaml
    • OPDRCNN-C:
      • --config-file configs/bmcc.yaml

  • Dataset:

    • OPDSynth:
      • The same to above
      • MODEL.WEIGHTS <BEST_ONLY_DET_MODEL>
    • OPDReal:
      • --data-path dataset/MotionDataset_h5_real
      • --model_attr_path dataset/MotionDataset_h5_real/real-attr.json
      • additional --opts (Add thie after 5000): MODEL.PIXEL_MEAN '[144.7425400388733, 131.67830996768458, 113.38040344244014, 975.0775146484375]' MODEL.PIXEL_STD '[20.100716763269578, 20.805474870130748, 23.863171739073888, 291.606201171875]'
      • MODEL.WEIGHTS <PRETRAIN_OPDSynth_MODEL>
      • PS: the LR for depth data is 0.0001 instead of 0.001 (the same to the paper describes)

Evaluation

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

python evaluate_on_log.py \
--config-file <MODEL_CONFIG> \
--output-dir eval_output \
--data-path <PATH_TO_DATASET> \
--input-format <RGB/depth/RGBD> \
--model_attr_path <PATH_TO_ATTR> \
--opts MODEL.WEIGHTS <PPRETRAINED_MODEL>

• Dataset needs the same options as above
• Model needs the same options as above
• Evaluate on test set: add things to --opts DATASETS.TEST "('MotionNet_test',)" (The complete version will be --opts MODEL.WEIGHTS <PPRETRAINED_MODEL> DATASETS.TEST "('MotionNet_test',)")
• Use inference result file instead of pretrained model: --inference-file <PATH_TO_INFERENCE_FILE>, this will directly evaluate using the results without inferencing again

Visualization

(Current code version need to download the dataset in raw format for visualization)

• Visualize the GT with 1000 random images in val set

  python render_gt.py \
  --output-dir vis_output \
  --data-path <PATH_TO_DATASET> \
  --valid-image <IMAGE_LIST_FILE> \

  • Dataset:
    • OPDSynth:
      • --data-path dataset/vis/MotionDataset_6.11
      • --valid-image dataset/vis/MotionDataset_6.11/val_1000.json
    • OPDReal:
      • --data-path dataset/vis/MotionDataset_real
      • --valid-image dataset/vis/MotionDataset_real/real_val_1000.json
      • --is-real

• Visualize the PREDICTION with 1000 random images in val set

  python render_pred.py \
  --output-dir vis_output \
  --data-path <PATH_TO_DATASET> \
  --model_attr_path <PATH_TO_ATTR> \
  --valid-image <IMAGE_LIST_FILE> \
  --inference-file <PATH_TO_INFERENCE_FILE> \
  --score-threshold 0.8 \
  --update-all 

  • Dataset:
    • OPDSynth: the same to above
    • OPDReal: the same to above
  • PS: inference file can be got after doing the evaluation