  A distinctive representation of image patches in form of features is a key
component of many computer vision and robotics tasks, such as image matching,
image retrieval, and visual localization. State-of-the-art descriptors, from
hand-crafted descriptors such as SIFT to learned ones such as HardNet, are
usually high dimensional; 128 dimensions or even more. The higher the
dimensionality, the larger the memory consumption and computational time for
approaches using such descriptors. In this paper, we investigate multi-layer
perceptrons (MLPs) to extract low-dimensional but high-quality descriptors. We
thoroughly analyze our method in unsupervised, self-supervised, and supervised
settings, and evaluate the dimensionality reduction results on four
representative descriptors. We consider different applications, including
visual localization, patch verification, image matching and retrieval. The
experiments show that our lightweight MLPs achieve better dimensionality
reduction than PCA. The lower-dimensional descriptors generated by our approach
outperform the original higher-dimensional descriptors in downstream tasks,
especially for the hand-crafted ones. The code will be available at
https://github.com/PRBonn/descriptor-dr.
