Stable Point-E

We implemented the training code based on OpenAI/Point-E. In terms of data, we provide colored point cloud data from the ShapeNet dataset along with descriptive text. As for the model, we offer a checkpoint trained on the aforementioned training data.

Data Preparation

You need to download pcl.tar from GoogleDrive and extract it to the data/shapenet folder.

Usage

Install with pip install -e ..

To get started with aforementioned training data:

• train.sh - training shell based on slurm, you can run as following: sh train.sh 32 train {your_slurm_partition} texts 5 point_e_save point_e/configs/shapenet.json

To visualize:

• vis.sh - sample point clouds, conditioned on text prompts, you can run as following: sh vis.sh {your_slurm_partition}

For P-FID and P-IS evaluation scripts, see:

• evaluate_pfid.py
• evaluate_pis.py

Acknowledgement

• Thanks OpenAI for implementing the Point-E codebase.

• Thanks Yongqiang Yao for the implementation of Distributed PyTorch.