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, 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/.