  The paper presents a learning-based method for computing a discriminative 3D
point cloud descriptor for place recognition purposes. Existing methods, such
as PointNetVLAD, are based on unordered point cloud representation. They use
PointNet as the first processing step to extract local features, which are
later aggregated into a global descriptor. The PointNet architecture is not
well suited to capture local geometric structures. Thus, state-of-the-art
methods enhance vanilla PointNet architecture by adding different mechanism to
capture local contextual information, such as graph convolutional networks or
using hand-crafted features. We present an alternative approach, dubbed
MinkLoc3D, to compute a discriminative 3D point cloud descriptor, based on a
sparse voxelized point cloud representation and sparse 3D convolutions. The
proposed method has a simple and efficient architecture. Evaluation on standard
benchmarks proves that MinkLoc3D outperforms current state-of-the-art. Our code
is publicly available on the project website:
https://github.com/jac99/MinkLoc3D
