  Moving object segmentation (MOS) provides a reliable solution for detecting
traffic participants and thus is of great interest in the autonomous driving
field. Dynamic capture is always critical in the MOS problem. Previous methods
capture motion features from the range images directly. Differently, we argue
that the residual maps provide greater potential for motion information, while
range images contain rich semantic guidance. Based on this intuition, we
propose MF-MOS, a novel motion-focused model with a dual-branch structure for
LiDAR moving object segmentation. Novelly, we decouple the spatial-temporal
information by capturing the motion from residual maps and generating semantic
features from range images, which are used as movable object guidance for the
motion branch. Our straightforward yet distinctive solution can make the most
use of both range images and residual maps, thus greatly improving the
performance of the LiDAR-based MOS task. Remarkably, our MF-MOS achieved a
leading IoU of 76.7% on the MOS leaderboard of the SemanticKITTI dataset upon
submission, demonstrating the current state-of-the-art performance. The
implementation of our MF-MOS has been released at
https://github.com/SCNU-RISLAB/MF-MOS.
