MinkUNeXt: Point Cloud-based Large-scale Place Recognition using 3D Sparse Convolutions
<|reference_start|>MinkUNeXt: Point Cloud-based Large-scale Place Recognition using 3D Sparse Convolutions: 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 and the In-house datasets. As a result, MinkUNeXt proves to outperform other methods in the state-of-the-art.<|reference_end|>
@article{cabrera2024minkunext:,
    title={MinkUNeXt: Point Cloud-based Large-scale Place Recognition using 3D
  Sparse Convolutions},
    author={J.J. Cabrera, A. Santo, A. Gil, C. Viegas and L. Pay'a},
    journal={arXiv preprint arXiv:2403.07593},
    year={2024},
    archivePrefix={arXiv},
    eprint={2403.07593},
    primaryClass={cs.CV}
}