CLIP-goes-3D

Official code for the paper "CLIP goes 3D: Leveraging Prompt Tuning for Language Grounded 3D Recognition"

arxiv / website

This repository includes the pre-trained models, evaluation and training codes for pre-training, zero-shot, and fine-tuning experiments of CG3D. It is built on the Point-BERT codebase. Please see the end of this document for a full list of code references.

To-Do:

• Setup
• Model weights from pre-training
• Model weights from fine-tuning
• Pre-training CG3D
• Zero-shot inference
• Fine-tune inference
• Fine-tuning CG3D
• Retrieval

Environment set-up

The known working environment configuration is

python 3.9
pytorch 1.12
CUDA 11.6

1. Install the conda virtual environment using the provided .yml file.

   conda env create -f environment.yml 
   

(OR)

1. Install dependencies manually.

   conda create -n cg3d
   conda activate cg3d
   

   pip install -r requirements.txt
   
   

   conda install -c anaconda scikit-image scikit-learn scipy
   

   pip install git+https://github.com/openai/CLIP.git
   

   pip install --upgrade https://github.com/unlimblue/KNN_CUDA/releases/download/0.2/KNN_CUDA-0.2-py3-none-any.whl
   

   cd ./extensions/chamfer_dist
   python setup.py develop
   

2. Build modified timm from scratch

   cd ./models/SLIP/pytorch-image-models
   pip install -e .
   

3. Install PointNet ops

   cd third_party/Pointnet2_PyTorch
   pip install -e .
   pip install pointnet2_ops_lib/.
   

4. Install PyGeM

cd third_party/PyGeM
python setup.py install

Dataset set-up

1. Download point cloud datasets for pre-training and fine-tuning.

• Download ShapeNetCore v2.

• Download ModelNet

• Download ScanObjectNN

  Save and unzip the above datasets.

2. Render views of textured CAD models of ShapeNet using this repository. We use a scale of 0.7 and 5 total views.

3. The data should be organized as

├── data (this may be wherever you choose)
│   ├── modelnet40_normal_resampled
│   │   │── modelnet10/40_shape_names.txt
│   │   │── modelnet10/40_train/test.txt 
│   │   │── airplane
│   │   │── ...
│   │   │── laptop 
│   ├── ShapeNet55
│   │   │── train.txt
│   │   │── test.txt
│   │   │── shapenet_pc
│   │   │   |── 03211117-62ac1e4559205e24f9702e673573a443.npy
│   │   │   |── ...
│   ├── shapenet_render
│   │   │── train_img.txt
│   │   │── val_img.txt
│   │   │── shape_names.txt
│   │   │── taxonomy.json
│   │   │── camera
│   │   │── img
│   │   │   |── 02691156
│   │   │   |── ...
│   ├── ScanObjectNN
│   │   │── main_split
│   │   │── ...

1) Model weights

a) Pre-trained CG3D weights

Download SLIP model weights from here.

PointTransformer

---

No. of points	Model file	Task	Configuration file
1024	download	Pre-training	link
8192	download	Pre-training	link

PointMLP

---

No. of points	Model file	Task	Configuration file
1024	download	Pre-training	link
8192	download	Pre-training	link

Test Zero-Shot performance

  python eval.py --config cfgs/ShapeNet55_models/{CONFIG} --exp_name {NAME FOR EXPT}  --ckpts {CKPT PATH} --slip_model {PATH TO SLIP MODEL} --zshot --npoints {1024,8192}

b) Fine-tuning model weights

PointTransformer

Dataset	Model Weights	TFBoard
ScanObjectNN	download	link
ModelNet	download	link

PointMLP

Dataset	Model Weights	TFBoard
ScanObjectNN	download	link
ModelNet	download	link

2) Training CG3D

a) Pre-training

• Change data paths to relevant locations in cfgs/dataset_configs/

• Pre-train PointTransformer on ShapeNet under the CG3D framework:

  python main.py  --exp_name {NAME FOR EXPT} --config cfgs/ShapeNet55_models/PointTransformerVPT.yaml  --pretrain    --out_dir {OUTPUT DIR PATH}  --text --image --clip --VL SLIP --visual_prompting --npoints 1024 --slip_model {PATH TO SLIP MODEL}
  
  

• Pre-train PointMLP on ShapeNet under the CG3D framework:

  python main.py  --exp_name {NAME FOR EXPT} --config cfgs/ShapeNet55_models/PointMLP_VPT.yaml  --pretrain    --out_dir {OUTPUT DIR PATH}  --text --image --clip --VL SLIP --visual_prompting --npoints 1024 --slip_model {PATH TO SLIP MODEL}
  
  

Zero-Shot Inference

python eval.py --config cfgs/ShapeNet55_models/{CONFIG} --exp_name {NAME FOR EXPT}  --ckpts {CKPT PATH} --slip_model {PATH TO SLIP MODEL} --zshot --npoints {1024,8192}

Fine-tuning Inference

python eval.py --config  cfgs/{ModelNet_models,ScanObjectNN_models}/{CONFIG} --exp_name {NAME FOR EXPT}  --ckpts {CKPT PATH} --npoints {1024,8192}

b) Fine-tuning

Finetuning PointTransformer:

CUBLAS_WORKSPACE_CONFIG=:4096:8 CUDA_VISIBLE_DEVICES=0 python finetune_cg3d.py --config cfgs/ModelNet_models/PointTransformer.yaml --exp_name {NAME OF EXPT} --finetune_model --ckpts {PATH OF PRETRAINED MODEL WEIGHTS} --dataset_root {PATH OF DATA STORAGE}

Finetuning PointMLP:

 CUBLAS_WORKSPACE_CONFIG=:4096:8 CUDA_VISIBLE_DEVICES=0 python finetune_cg3d.py --config cfgs/ModelNet_models/PointMLP.yaml --exp_name {NAME OF EXPT} --finetune_model --ckpts {PATH OF PRETRAINED MODEL WEIGHTS} --dataset_root {PATH OF DATA STORAGE}

Please change the .yml files to change the finetuning dataset from ModelNet to ScanObjectNN, etc.

References

• PointBert
• PointMLP
• SLIP
• CLIP
• Pointnet2_PyTorch

Citation

@article{hegde2023clip,
 title={CLIP goes 3D: Leveraging Prompt Tuning for Language Grounded 3D Recognition},
 author={Hegde, Deepti and Valanarasu, Jeya Maria Jose and Patel, Vishal M},
 journal={arXiv preprint arXiv:2303.11313},
 year={2023}
}