Create README.md

README.md

@@ -0,0 +1,65 @@
+# S2O: Static to Openable Enhancement for Articulated 3D Objects
+
+Denys Iliash<sup>1</sup>,
+[Hanxiao Jiang](https://jianghanxiao.github.io)<sup>2</sup>,
+[Yiming Zhang](https://scholar.google.ca/citations?user=scUaE38AAAAJ&hl=en)<sup>1</sup>,
+[Manolis Savva](https://msavva.github.io)<sup>1</sup>,
+[Angel X. Chang](https://angelxuanchang.github.io/)<sup>1,3</sup>
+
+<sup>1</sup>Simon Fraser University, <sup>2</sup>Columbia University, <sup>3</sup>Canada-CIFAR AI Chair, Amii
+
+### [Project Page](https://3dlg-hcvc.github.io/s2o/)
+
+This repo contains the code for S2O paper. Data can be found on [HuggingFace](https://huggingface.co/datasets/3dlg-hcvc/s2o).
+
+
+<img src='docs/static/images/teaser.png'/>
+
+## Installation
+
+      git clone --recursive git@github.com:3dlg-hcvc/s2o.git
+      
+      conda create -f environment.yml
+      conda activate s2o
+
+Additionally, follow instructions in the submodules you would like to use in order to install additionally-required libraries and build some dependencies from source.
+
+## Data
+
+Data can be found on [HuggingFace](https://huggingface.co/datasets/3dlg-hcvc/s2o). 
+
+## Inference
+For PC-based methods run:
+
+    # Pre-processing
+    python scripts/preprocess/create_subset_points.py --data_path {path/to/pcd/downsample.h5} --dat_json {path/to/split/json}
+
+    # For all PG 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}
+
+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
+
+## Evaluation
+PC metrics are obtained from minsu3d eval.py and OC-cost demo.py, follow instructions from the submodules to 
+
+    # 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}
+