Abstract
The costly and time-consuming annotation process to produce large training
sets for modelling semantic LiDAR segmentation methods has motivated the
development of semi-supervised learning (SSL) methods. However, such SSL
approaches often concentrate on employing consistency learning only for
individual LiDAR representations. This narrow focus results in limited
perturbations that generally fail to enable effective consistency learning.
Additionally, these SSL approaches employ contrastive learning based on the
sampling from a limited set of positive and negative embedding samples. This
paper introduces a novel semi-supervised LiDAR semantic segmentation framework
called ItTakesTwo (IT2). IT2 is designed to ensure consistent predictions from
peer LiDAR representations, thereby improving the perturbation effectiveness in
consistency learning. Furthermore, our contrastive learning employs informative
samples drawn from a distribution of positive and negative embeddings learned
from the entire training set. Results on public benchmarks show that our
approach achieves remarkable improvements over the previous state-of-the-art
(SOTA) methods in the field. The code is available at:
https://github.com/yyliu01/IT2.