Synthesizing 3D point cloud data is an open area of research with the potential to provide a source of infinite amounts of diverse data. In 3D, synthetic data may take the form of meshes, voxels, or raw point clouds in order to learn a representation that enables the solution of computer vision tasks such as classification, segmentation, and reconstruction. Although color and geometry are among the defining features of 3D objects in the physical world, current point cloud generation methods either create the geometry or the color of the point cloud, but not both.
This repository provides source code for our 2020 3DV paper titled "A Progressive Conditional Generative Adversarial Network for Generating Dense and Colored 3D Point Clouds." Our progressive conditional generative adversarial network (PCGAN) creates dense 3D point clouds, with color, for assorted classes of objects in an unsupervised manner. To overcome the difficulty of capturing intricate details at high resolutions, we propose a point transformer that progressively grows the network through the use of graph convolutions. The network is composed of a leaf output layer and an initial set of branches. Every training iteration evolves a point vector into a point cloud of increasing resolution. After a fixed number of iterations, the number of branches is increased by replicating the last branch.
If you find this project useful, then please consider citing our work.
@inproceedings{arshad2020progressive,
title={A Progressive Conditional Generative Adversarial Network for Generating Dense and Colored 3D Point Clouds},
author={Arshad, Mohammad Samiul and Beksi, William J},
booktitle={Proceedings of the International Conference on 3D Vision (3DV)},
year={2020},
pages={712--722},
organization={IEEE}
}
PCGAN has the following dependencies:
- Python 3.6+ with Numpy, Scipy, and Matplotlib
- PyTorch (version 1.3+)
- opencv-python
The code has been tested with Python 3.6, PyTorch 1.3.1, and CUDA 9.2 on Ubuntu 19.04.
We use the ShapeNetCore dataset as the source of our 3D models. First, download and unzip the 3D models for the appropriate class (e.g., chair) in the indexed directory:
./datasets/shapenet/mesh/[class-id]
Next, remove all instances of models without mtl files and then use
./data/dataset_preparation.py to extract the colored point clouds of a desired
resolution. Lastly, store the extracted point clouds in the indexed directory:
./datasets/shapenet/point-clouds/[class-id]
In PCGAN, arguments.py contains nearly all the training parameters. To train a
model, execute the following command:
python3 train.py
PCGAN utilizes code from the following projects:
