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Hyungtae-Lim/BUFFER-X
BUFFER-X is a robotics model from Hyungtae-Lim. Use it for the robotics task on the model card, and read the license before you ship it in a product. It is set up for pytorch. The card lists the license as mit.
BUFFER-X is a PyTorch model for zero-shot point cloud registration across indoor, outdoor, homogeneous, and heterogeneous sensor settings.
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Updated May 9, 2026
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From the Hugging Face model README
BUFFER-X is a PyTorch model for zero-shot point cloud registration across indoor, outdoor, homogeneous, and heterogeneous sensor settings.
This Hugging Face repository is intended to host the pretrained BUFFER-X snapshots. The code lives in the official GitHub repository:
https://github.com/MIT-SPARK/BUFFER-X
The repository metadata uses the MIT license to match the included LICENSE file.
If you release model weights under different terms, update the YAML metadata before
uploading.
Upload pretrained weights under the same layout used by the GitHub code:
snapshot/
threedmatch/
Desc/best.pth
Pose/best.pth
kitti/
Desc/best.pth
Pose/best.pth
The included upload helper preserves this layout automatically when a local
snapshot/ directory exists.
Install BUFFER-X, then download the pretrained snapshots from this model repo:
git clone https://github.com/MIT-SPARK/BUFFER-X
cd BUFFER-X
./scripts/install.sh --cuda cu124 --with-hub
python scripts/download_pretrained_models.py --source hf --repo-id <this-model-repo>
Run evaluation after preparing the datasets:
python test.py --dataset 3DMatch TIERS Oxford MIT --experiment_id threedmatch --verbose
BUFFER-X inference uses CUDA-specific dependencies, including pointnet2_ops,
KNN_CUDA, custom C++ wrappers, and torch-batch-svd. The GitHub installation
script installs these pieces for supported PyTorch/CUDA combinations.
@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}
}