BUFFER-X (ICCV 2025, 🌟Highlight🌟)

Minkyun Seo*, Hyungtae Lim*, Kanghee Lee, Luca Carlone, Jaesik Park

Towards zero-shot and beyond! 🚀
Official repository of BUFFER-X, a zero-shot point cloud registration method
across diverse scenes without retraining or tuning.

After cloning this repository:

git clone https://github.com/MIT-SPARK/BUFFER-X && cd BUFFER-X

Setup your own virtual environment (e.g., conda create -n bufferx python=3.x or setting your Nvidia Docker env.) and then install the required libraries. We present some shellscripts as follows.

[Python 3.8, Pytorch 1.9.1, CUDA 11.1 on Ubuntu 22.04]

./scripts/install_py3_8_cuda11_1.sh

[Python 3.10, Pytorch 2.7.1, CUDA 11.8, Cudnn 9.1.0 on Ubuntu 24.04]

./scripts/install_py3_10_cuda11_8.sh

[Python 3.11, Pytorch 2.6.0, CUDA 12.4, Cudnn 9.1.0 on Ubuntu 24.04]

./scripts/install_py3_11_cuda12_4.sh

You can easily run our generalization benchmark with BUFFER-X. First, download the model using the following script:

./scripts/download_pretrained_models.sh

Next, to evaluate BUFFER-X in diverse scenes, please download the preprocessed data by running the following command. It requires around 130 GB. However, to include all other datasets (i.e., Scannetpp_iphone, Scannetpp_faro), approximately 150 GB more storage is required. Due to the data copyrights, we cannot provide preprocessed data for ScanNet++, so if you want to reproduce whole results, please refer to here

./scripts/download_all_data.sh

Then, you can run the below command as follows:

python test.py --dataset <LIST OF DATASET NAMES> --experiment_id <EXPERIMENT ID> --verbose

Experiment ID refers to the saved model’s filename. Provided snapshots include threedmatch and kitti, each trained on the corresponding dataset.

e.g.,

python test.py --dataset 3DMatch TIERS Oxford MIT --experiment_id threedmatch --verbose

You can also run the evaluation script for all datasets at once by using the provided script:

./scripts/eval_all.sh <EXPERIMENT ID>

BUFFER-X supports training on either the 3DMatch or KITTI dataset. As un example, run the following command to train the model:

python train.py --dataset 3DMatch

If you find our work useful in your research, please consider citing:

@article{Seo_BUFFERX_arXiv_2025,
Title={BUFFER-X: Towards Zero-Shot Point Cloud Registration in Diverse Scenes},
Author={Minkyun Seo and Hyungtae Lim and Kanghee Lee and Luca Carlone and Jaesik Park},
Journal={2503.07940 (arXiv)},
Year={2025}
}

This work was supported by IITP grant (RS-2021-II211343: AI Graduate School Program at Seoul National University) (5%), and by NRF grants funded by the Korea government (MSIT) (No. 2023R1A1C200781211 (65%) and No. RS-2024-00461409 (30%), respectively).

In addition, we appreciate the open-source contributions of previous authors, and especially thank Sheng Ao, the first author of BUFFER, for allowing us to use the term 'BUFFER' as part of the title of our study.

• FCGF
• Vector Neurons
• D3Feat
• PointDSC
• SpinNet
• GeoTransformer
• RoReg
• BUFFER

• 25/07/2025: This work is selected as a Highlight paper at ICCV 2025.
• 25/06/2025: This work is accepted by ICCV 2025.