Discriminative model

Theoretical computer science

Operating system

Set (abstract data type)

Computer science

Cloud computing

Visual arts

Code (set theory)

Architecture

Reverse engineering

Point cloud

Graph

Pattern recognition (psychology)

Mathematics

Politics

Political science

Artificial intelligence

Geometry

Representation (politics)

Point (geometry)

Programming language

Convolutional neural network

The paper presents a learning-based method for computing a discriminative 3D point cloud descriptor for place recognition purposes. Existing methods, such as Point-NetVLAD, 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 Min-kLoc3D, 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. <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sup>

MinkLoc3D: Point Cloud Based Large-Scale Place Recognition