Awesome
Cloud Transformers: A Universal Approach To Point Cloud Processing Tasks
This is an official PyTorch code repository of the paper "Cloud Transformers: A Universal Approach To Point Cloud Processing Tasks " (ICCV, 2021).
Here, we present a versatile point cloud processing block that yields state-of-the-art results on many tasks.
The key idea is to process point clouds with many cheap low-dimensional different projections followed by standard convolutions. And we do so both in parallel and sequentially.
Datasets
We provide links to the datasets we used to train/evaluate. After unpacking and preparation, please edit the dataset path (data:path
field) in configs/*.yaml
- ShapeNet Single-View 3D Reconstruction
- ShapeNet Inpainting (GRNet version)
- ScanObjectNN
- S3DIS KPConv protocol
- S3DIS 1 x 1 protocol
Pre-trained models
We provide our pre-trained models' weights in a single archive.
Building Dependencies
To install and build all the modules required, please run:
bash ./install_deps.sh
Code Structure
In layers/cloud_transform.py
the core operations are implemented (rasterization Splat
and de-rasterization Slice
).
While in layers\mutihead_ct_*.py
we provide slightly different versions of Multi-Headed Cloud Transform (MHCT).
The model zoo is situated in model_zoo
, where the models for corresponding tasks are constructed of Multi-Headed Cloud Transforms.
Run
We train our models in multi-GPU setting using DistributedDataParallel. To train on n
GPUs, please run the following commands:
python train_${SCRIPT_NAME}.py ${EXP_NAME} -c configs/${CONFIG_NAME}.yaml --master localhost:3315 --rank 0 --num_nodes n
...
python train_${SCRIPT_NAME}.py ${EXP_NAME} -c configs/${CONFIG_NAME}.yaml --master localhost:3315 --rank <n-1> --num_nodes n
The semantics for evaluation scripts is almost the same:
python eval_${SCRIPT_NAME}.py ${EXP_NAME} -c configs/eval/${CONFIG_NAME}.yaml
Cite
If you find our work helpful, please do not hesitate to cite us.
@inproceedings{mazur2021cloudtransformers,
title={Cloud Transformers: A Universal Approach To Point Cloud Processing Tasks},
author={Mazur, Kirill and Lempitsky, Victor},
booktitle={International Conference on Computer Vision (ICCV)},
year={2021}
}