  Weakly supervised point cloud semantic segmentation methods that require 1\%
or fewer labels, hoping to realize almost the same performance as fully
supervised approaches, which recently, have attracted extensive research
attention. A typical solution in this framework is to use self-training or
pseudo labeling to mine the supervision from the point cloud itself, but ignore
the critical information from images. In fact, cameras widely exist in LiDAR
scenarios and this complementary information seems to be greatly important for
3D applications. In this paper, we propose a novel cross-modality weakly
supervised method for 3D segmentation, incorporating complementary information
from unlabeled images. Basically, we design a dual-branch network equipped with
an active labeling strategy, to maximize the power of tiny parts of labels and
directly realize 2D-to-3D knowledge transfer. Afterwards, we establish a
cross-modal self-training framework in an Expectation-Maximum (EM) perspective,
which iterates between pseudo labels estimation and parameters updating. In the
M-Step, we propose a cross-modal association learning to mine complementary
supervision from images by reinforcing the cycle-consistency between 3D points
and 2D superpixels. In the E-step, a pseudo label self-rectification mechanism
is derived to filter noise labels thus providing more accurate labels for the
networks to get fully trained. The extensive experimental results demonstrate
that our method even outperforms the state-of-the-art fully supervised
competitors with less than 1\% actively selected annotations.
