ConDaFormer

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[NeurIPS'23] ConDaFormer: Disassembled Transformer with Local Structure Enhancement for 3D Point Cloud Understanding

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# ConDaFormer: Disassembled Transformer with Local Structure Enhancement for 3D Point Cloud Understanding

This is the official implementation of our [NeurIPS 2023 paper](https://openreview.net/forum?id=kKXJkiniOx).

## Installation

Clone the repository:
```bash
git clone https://github.com/LHDuan/ConDaFormer.git
```
Create a conda virtual environment and install the corresponding dependencies according to [Point Transformer V2](https://github.com/Pointcept/PointTransformerV2/) (we test on python=3.8.15, pytorch==1.10.1, cuda==11.1, spconv-cu113==2.3.3).

Install 'sptr' for Disassembled Transformer:
```bash
# sptr is modified from https://github.com/dvlab-research/SphereFormer
conda activate pcr
cd libs/sptr
python setup.py install
```

Modify the spconv code to support depth-wise sparse convolution.
```bash
# PATH is the path of your pcr conda env.
# This operation replaces /PATH/pcr/lib/python3.8/site-packages/spconv/pytorch/conv.py with libs/spconv/conv.py. 
cp libs/spconv/conv.py /PATH/pcr/lib/python3.8/site-packages/spconv/pytorch/
```

## Data Preparation
Please follow the instructions in [Point Transformer V2](https://github.com/Pointcept/PointTransformerV2/) to prepare the ScanNet v2 and S3DIS datasets and link processed dataset to codebase.

The structure of the data folder should be
```
data/
  --scannet
    --train/scene*.pth
    --val/scene*.pth
    --test/scene*.pth
  --s3dis
    --Area_1/*.pth
    --Area_2
    --**
```

## Training

Run the following command to train our ConDaFormer.
The command will create an experiment folder according to the dataset name('-d') and experiment name('-n').
The traning config, log, checkpoints and some essential code will be save into the exp folder.
```bash
# You can adjust the gpu number according to you GPU's memory.
# For S3DIS
CUDA_VISIBLE_DEVICES=0,1 sh scripts/train.sh -g 2 -d s3dis -c condaformer -n condaformer
# For ScanNet20
CUDA_VISIBLE_DEVICES=0,1,2,3 sh scripts/train.sh -g 4 -d scannet -c condaformer -n condaformer
# For ScanNet200
CUDA_VISIBLE_DEVICES=0,1,2,3 sh scripts/train.sh -g 4 -d scannet200 -c condaformer -n condaformer
```

## Evaluation
Download the pretrained model [here](https://drive.google.com/drive/folders/13VF0-B6165XALgJmocqbZyzU5Dzs1dD4?usp=sharing) 
and place it under the 'ckpts/' folder.
Run the following command to evaluate the performance:
```bash
# With test-time-augmentation
# For S3DIS
CUDA_VISIBLE_DEVICES=0 sh ./scripts/test.sh -d s3dis -c condaformer -n test -w ./ckpts/s3dis.pth
# For ScanNet20
CUDA_VISIBLE_DEVICES=0 sh ./scripts/test.sh -d scannet -c condaformer -n test -w ./ckpts/scannet.pth
# For ScanNet200
CUDA_VISIBLE_DEVICES=0 sh ./scripts/test.sh -d scannet200 -c condaformer -n test -w ./ckpts/scannet200.pth
```

## Citations
If you find our work useful in your research, please consider citing:
```
@inproceedings{duan2023condaformer,
  title={ConDaFormer: Disassembled Transformer with Local Structure Enhancement for 3D Point Cloud Understanding},
  author={Duan, Lunhao and Zhao, Shanshan and Xue, Nan and Gong, Mingming and Xia, Gui-Song and Tao, Dacheng},
  booktitle={Thirty-seventh Conference on Neural Information Processing Systems},
  year={2023}
}
```

## Contact
[lhduan@whu.edu.cn](lhduan@whu.edu.cn)

## Acknowledgements
We thank the authors of [Point Transformer V2](https://github.com/Pointcept/PointTransformerV2) and [SphereFormer](https://github.com/dvlab-research/SphereFormer). Our implementation is heavily built upon their codes.

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