This repository contains the experimental PyTorch implementation of the paper:

Generating Point Cloud from Single Image in the Few Shot Scenario
Yu Lin, Jinghui Guo, Yang Gao, Yifan Li, Zhuoyi Wang, Latifur Khan
In ACM MM 2021

In this project, we are focusing on Few shot Point cloud Single image Generation (FPSG) using class-specific & class-agnostic shape prior. Specifically, we abstract each class by a prototype vector that embeds class-specific shape priors. Class-agnostic shape priors are modeled by a set of learnable shape primitives that encode universal 3D shape information shared across classes. Later, we combine the input image with class-specific prototypes and class-agnostic shape primitives to guide the point cloud generation process. Experiment results demonstrate the superior performance of the proposed method.

There are three branches from top to bottom: 1) Image branch, 2) Class-specific shape prior branch and 3) Class-agnostic shape prior branch.

1. Clone this repo:

git clone https://github.com/voidstrike/FPSG.git

2. Install the dependencies:

• Python 3.6
• CUDA 11.0
• G++ or GCC5
• PyTorch. Codes are tested with version 1.8.0
• Kaolin 0.9.0
• Pymesh 1.0.2

ModelNet dataset can be downloaded from ModelNet_views and ModelNet_pcs. Unzip the downloaded file to your preferred directory. The 2D projections of ModelNet are from MVCNN

For the ShapeNet dataset, please download the dataset from their website and render it via Blender. We provide src/view_generator.py and src/train_test_split.py for the image generation and TRAIN/TEST split, respectively. Corresponding phong.py and background.png are also attached, please note that the provided code is compatible with Blender v2.79

Take ModelNet for example.

• Prepare the dataset (you will get two files: modelnet_train.txt and modelnet_test.txt, and one folder modelnet_files/ with auxiliary files ). Please check the source code if you want to use different set of classes (categories)

python3 generate_dataset.py --img_path IMG_PATH --pc_path PC_PATH --dataset modelnet

• Train a model

# General
CUDA_VISIBLE_DEVICES=X python3 trainNetwork.py --config_path TRAIN_CONFIG --test_path TEST_CONFIG --pc_encoder_path PRETRAINED_ENCODER --n_way 1 --n_shot [1-32] --name EXP_NAME --intra_recon

# Concrete
CUDA_VISIBLE_DEVICES=0 python3 trainNetwork.py --config modelnet_train.txt --test_path modelnet_test.txt --pc_encoder_path ../checkpoint/pretrain_pointnet/pretrained_pcencoder_pointnet.pt --n_way 1 --n_shot 32 --n_query 5 --num_cluster --epoch 400 --lr 0.0001 --name modelnet_exp

• Noted that we use the PRETRAINED_ENCODER in the command, you can also train such an encoder by running src/trainPointAE.py, classes (categories) are also specified in the source code.

• There are more hyper parameter options, please refer to the source code for more detail. For example, --n_episode and --n_cluster

• Please remember to modify the CUDA device number X, TRAIN_CONFIG, TEST_CONFIG and PRETRAINED_ENCODER accordingly.

• Eval a model (Actually, the evaluation code will be executed at the end of the training)

CUDA_VISIBLE_DEVICES=X python3 evaluae_Network.py --config_PATH TRAIN_CONFIG --test_path TEST_CONFIG --pc_encoder_path PRETRAINED_ENCODER --n_way 1 --n_shot ANYNUMBER --name EXP_NAME

• There are more hyper parameter options, please refer to the source code for more detail. For example, --sequential_eval denotes the evaluation mode.

Some codes are hard-coded so you may need to modify them based on your system and data location.

For example:

src/datasets/modelnet.py L112 & src/datasets/shapenet.py L132 -- Modified the index number based on your file path

More example will be added later, if any.

If you use this code for your research, please consider cite our papers:

@inproceedings{lin2021generating,
  title={Generating Point Cloud from Single Image in The Few Shot Scenario},
  author={Lin, Yu and Guo, Jinghui and Gao, Yang and Li, Yi-fan and Wang, Zhuoyi and Khan, Latifur},
  booktitle={Proceedings of the 29th ACM International Conference on Multimedia},
  pages={2834--2842},
  year={2021}
}

and

@inproceedings{lin2021single,
  title={Single View Point Cloud Generation via Unified 3D Prototype},
  author={Lin, Yu and Wang, Yigong and Li, Yi-Fan and Wang, Zhuoyi and Gao, Yang and Khan, Latifur},
  booktitle={Proceedings of the AAAI Conference on Artificial Intelligence},
  volume={35},
  number={3},
  pages={2064--2072},
  year={2021}
}