This paper presents MinkUNeXt, an effective and efficient architecture for place-recognition from point clouds entirely based on the new 3D MinkNeXt Block, a residual block composed of 3D sparse convolutions that follows the philosophy established by recent Transformers but purely using simple 3D convolutions. Feature extraction is performed at different scales by a U-Net encoder-decoder network and the feature aggregation of those features into a single descriptor is carried out by a Generalized Mean Pooling (GeM). The proposed architecture demonstrates that it is possible to surpass the current state-of-the-art by only relying on conventional 3D sparse convolutions without making use of more complex and sophisticated proposals such as Transformers, Attention-Layers or Deformable Convolutions. A thorough assessment of the proposal has been carried out using the Oxford RobotCar, the In-house, the KITTI and the USyd datasets. As a result, MinkUNeXt proves to outperform other methods in the state-of-the-art.
@article{cabrera2025minkunext,
title = {MinkUNeXt: Point cloud-based large-scale place recognition using 3D sparse convolutions},
journal = {Array},
volume = {28},
pages = {100569},
year = {2025},
issn = {2590-0056},
doi = {https://doi.org/10.1016/j.array.2025.100569},
url = {https://www.sciencedirect.com/science/article/pii/S2590005625001961},
author = {Juan José Cabrera and Antonio Santo and Arturo Gil and Carlos Viegas and Luis Payá},
keywords = {Place recognition, LiDAR, Point cloud embedding, 3D sparse convolutions},
abstract = {This paper presents MinkUNeXt, an effective and efficient architecture for place-recognition from point clouds entirely based on the new 3D MinkNeXt Block, a residual block composed of 3D sparse convolutions that follows the philosophy established by recent Transformers but purely using simple 3D convolutions. Feature extraction is performed at different scales by a U-Net encoder–decoder network and the feature aggregation of those features into a single descriptor is carried out by a Generalized Mean Pooling (GeM). The proposed architecture demonstrates that it is possible to surpass the current state-of-the-art by only relying on conventional 3D sparse convolutions without making use of more complex and sophisticated proposals such as Transformers, Attention-Layers or Deformable Convolutions. A thorough assessment of the proposal has been carried out using the Oxford RobotCar, the In-house, the KITTI and the USyd datasets. As a result, MinkUNeXt proves to outperform other methods in the state-of-the-art. The implementation is publicly available at https://juanjo-cabrera.github.io/projects-MinkUNeXt/.}
}The Ministry of Science, Innovation and Universities (Spain) has funded this work through FPU21/04969 (J.J. Cabrera). This work is part of the projects PID2023-149575OB-I00, funded by MICIU/AEI/10.13039/501100011033 and by FEDER UE, and CIPROM/2024/8, funded by Generalitat Valenciana.
