  The local reference frame (LRF) acts as a critical role in 3D local shape
description and matching. However, most of existing LRFs are hand-crafted and
suffer from limited repeatability and robustness. This paper presents the first
attempt to learn an LRF via a Siamese network that needs weak supervision only.
In particular, we argue that each neighboring point in the local surface gives
a unique contribution to LRF construction and measure such contributions via
learned weights. Extensive analysis and comparative experiments on three public
datasets addressing different application scenarios have demonstrated that
LRF-Net is more repeatable and robust than several state-of-the-art LRF methods
(LRF-Net is only trained on one dataset). In addition, LRF-Net can
significantly boost the local shape description and 6-DoF pose estimation
performance when matching 3D point clouds.
