⚠️ This repo has been integrated into our new benchmark PointCloud-C! Please checkout PointCloud-C for any future updates!

Code for the paper "Benchmarking and Analyzing Point Cloud Classification under Corruptions". For the latest updates, see: sites.google.com/view/modelnetc/home

Benchmarking and Analyzing Point Cloud Classification under Corruptions
Jiawei Ren, Liang Pan, Ziwei Liu

arXiv 2022

ModelNet-C [Download Link]

git clone https://github.com/jiawei-ren/ModelNet-C.git
cd ModelNet-C

Set up the environment by:

conda create --name modelnetc python=3.7.5
conda activate modelnetc
pip install -r requirements.txt
cd SimpleView/pointnet2_pyt && pip install -e . && cd -
pip install -e modelnetc_utils

Download ModelNet-C by:

cd data
gdown https://drive.google.com/uc?id=1KE6MmXMtfu_mgxg4qLPdEwVD5As8B0rm
unzip modelnet_c.zip && cd ..

Alternatively, you may download ModelNet40-C manually and extract it under data.

Download pretrained models by

gdown https://drive.google.com/uc?id=11RONLZGg0ezxC16n57PiEZouqC5L0b_h
unzip pretrained_models.zip

Alternatively, you may download pretrained models manually and extract it under root directory.

Evaluation commands are provided in EVALUATE.md.

We have provided evaluation utilities to help you evaluate on ModelNet-C using your own codebase. Please follow CUSTOMIZE.md.

*Standalone indicates if the method is a standalone architecture or a combination with augmentation or pretrain.

• PointMixup
• OcCo
• PointBERT

@article{
    ren2022modelnetc,
    title={Benchmarking and Analyzing Point Cloud Classification under Corruptions},
    author={Jiawei Ren and Liang Pan and Ziwei Liu},
    journal={arXiv:2202.03377},
    year={2022},
}

This codebase heavily borrows codes from the following repositories:

• SimpleView
• PCT_Pytorch
• GDANet
• CurveNet
• PAConv
• RSMix
• PointWOLF