Offical Pytorch Implementation of CVPR2024 KP-RED: Exploiting Semantic Keypoints for Joint 3D Shape Retrieval and Deformation
Install using conda:
conda env create -f environment.yml
conda activate kpred
Download ShapeNet to data/shapenet/shape_data.
To train the deformation module on the chair category with input of full target point clouds run:
python scripts/main.py -c configs/chair-full.yaml
Then, to train the deformation module on the chair category with input of partial target point clouds run:
python scripts/main.py -c configs/chair-partial.yaml
The option ckpt should be customized as the path of trained deformation model for full point clouds.
Finally, to train the retrieval module run:
python scripts/main.py -c configs/chair-retrieval.yaml
The option ckpt should be customized as the path of trained deformation model for full point clouds.
To test the trained R&D model on full point clouds run:
python scripts/main.py -c configs/chair-full.yaml -t configs/test.yaml
The option ckpt should be customized as the path of trained deformation model for full point clouds.
The option latent_ckpt should be customized as the path of trained retrieval model.
To test the trained R&D model on partial point clouds on PartNet run:
python scripts/main.py -c configs/chair-full.yaml -t configs/test_partial.yaml
The option ckpt should be customized as the path of trained deformation model for partial point clouds.
The option latent_ckpt should be customized as the path of trained retrieval model.
The option points_dir should be customized as the path for storing the generated partial point clouds.
The option test_partial_ratio should be set from 0 to 1.
To visualize the results run:
python browse3d/browse3d.py --log_dir logs/chair/test --port 5050
and open localhost:5050 in your web browser.
Our implementation leverages the code from KeypointDeformer.