S2O: Static to Openable Enhancement for Articulated 3D Objects

Denys Iliash¹, Hanxiao Jiang², Yiming Zhang¹, Manolis Savva¹, Angel X. Chang^1,3

¹Simon Fraser University, ²Columbia University, ³Canada-CIFAR AI Chair, Amii

Project Page

This repo contains the code for S2O paper. Data can be found on HuggingFace.

In the Static to Openable (S2O) task, we aim to convert static meshes of container objects to articulated openable objects.

We develop a three stage pipeline consisting of 1) part segmentation, 2) motion prediction, and 3) interior completion.

Installation

git clone --recursive git@github.com:3dlg-hcvc/s2o.git
  
conda env create -f environment.yml
conda activate s2o

Additionally, follow instructions in the submodules you would like to use in order to install required libraries and build some dependencies from source.

Data

Data and checkpoints can be found on HuggingFace.

Please request access. After you are approved, you can download the data with git lfs.

git lfs install
git clone git@hf.co:datasets/3dlg-hcvc/s2o

Running the Static to Openable Pipeline

Preparing your asset

• TODO: Add instructions for point sampling a new asset and preparing it for segmentation

Part Segmentation

We explore different methods (point cloud based, image based, and mesh based) for identifying openable parts and segmenting out the parts from the mesh.

We provide checkpoints for the different models at https://huggingface.co/datasets/3dlg-hcvc/s2o. Below we provide a summary of the different models, code directory, and their part segmentation performance. We recommend using the PointGroup + PointNeXT + FPN model.

Type	Method	code	weights	F1 on PM-Openable	F1 on ACD
PC	PointGroup + U-Net	minsu3d	pg_unet.ckpt	21.1	4.9
PC	PointGroup + Swin3D	Pointcept	pg_swin3d.pth	29.6	9.4
PC	PointGroup + PointNeXT + FPN	internal_pg	pg_px_fpn.ckpt	78.5	13.3
PC	Mask3D	Mask3D	mask3d.ckpt	42.9	4.8
Mesh	MeshWalker	MeshWalker	meshwalker.keras	0.8	0.7
Image	OPDFormer	OPDMulti	opdformer_p.pth	18.6	7.8

For PC-based methods run:

# Pre-processing
python scripts/preprocess/create_subset_points.py --data_path {path/to/pcd/downsample.h5} --data_json {path/to/split/json}

# For all PointGroup methods convert to minsu3d format
python scripts/preprocess/prepare_for_minsu3d.py --data_path {path/to/pcd-subset/downsample.h5} --data_json {path/to/split/json}

Follow submodule instructions for inference. Then, for post-processing and mapping run:

# Post-processing

# Map predictions from subset to full point clouds
python scripts/postprocess/map_predictions_from_subset_points.py --exp_dir {path/to/predictions} --data_path {path/to/pcd/downsample.h5} --subset_path {path/to/pcd-subset/downsample.h5} --output_path {path/to/full/predictions}

# Map full predictions to mesh, use --gt flag with this script to generate gt for evaluation
python scripts/postprocess/map_pc_to_mesh.py --{path/to/full/predictions} --data_path {path/to/processed_mesh} --data_json {path/to/split/json} --sampled_data {path/to/pcd/downsample.h5} --output_dir {path/to/mapped/meshes/output}

Motion prediction

To run heuristic motion prediction:

python motion_inference.py --pred_path {path/to/mapped/meshes/output} --output_path {path/to/mapped/meshes/output/motion} --export

Interior Completion

• TODO: Provide instructions for interior completion
• TODO: Provide instructions for exporting articulated mesh as GLB / URDF
• TODO: Provide instructions for visualizing articulated mesh

Reproducing Experiments

Evaluation

PC metrics are obtained from minsu3d eval.py and OC-cost demo.py, follow the instructions from the submodules. For mesh segmentation and motion prediction evaluation:

# GT is obtained from running map_pc_to_mesh with --gt flag
python mesh_eval.py --predict_dir {path/to/mapped/meshes/output} --gt_path {path/to/preprocessed/gt} --output_dir {dir/for/logged/metrics} --data_json {path/to/split/json} --glb_path {path/to/processed_mesh}

# For metrics from the supplement
python mesh_eval_seg.py --predict_dir {path/to/mapped/meshes/output} --gt_path {path/to/preprocessed/gt} --output_dir {dir/for/logged/metrics} --data_json {path/to/split/json} --glb_path {path/to/processed_mesh}

# For motion evaluation
python motion_eval.py --predict_dir {path/to/mapped/meshes/output} --output_dir {dir/for/logged/metrics} --data_json {path/to/split/json} --glb_path {path/to/processed_mesh}

Training

• TODO: Provide training instructions

Citation

Please cite our work if you use S2O results/code or ACD dataset.

@article{iliash2024s2o,
  title={{S2O}: Static to openable enhancement for articulated {3D} objects},
  author={Iliash, Denys and Jiang, Hanxiao and Zhang, Yiming and Savva, Manolis and Chang, Angel X},
  journal={arXiv preprint arXiv:2409.18896},
  year={2024}
}