Whilst the availability of 3D LiDAR point cloud data has significantly grown
in recent years, annotation remains expensive and time-consuming, leading to a
demand for semi-supervised semantic segmentation methods with application
domains such as autonomous driving. Existing work very often employs relatively
large segmentation backbone networks to improve segmentation accuracy, at the
expense of computational costs. In addition, many use uniform sampling to
reduce ground truth data requirements for learning needed, often resulting in
sub-optimal performance. To address these issues, we propose a new pipeline
that employs a smaller architecture, requiring fewer ground-truth annotations
to achieve superior segmentation accuracy compared to contemporary approaches.
This is facilitated via a novel Sparse Depthwise Separable Convolution module
that significantly reduces the network parameter count while retaining overall
task performance. To effectively sub-sample our training data, we propose a new
Spatio-Temporal Redundant Frame Downsampling (ST-RFD) method that leverages
knowledge of sensor motion within the environment to extract a more diverse
subset of training data frame samples. To leverage the use of limited annotated
data samples, we further propose a soft pseudo-label method informed by LiDAR
reflectivity. Our method outperforms contemporary semi-supervised work in terms
of mIoU, using less labeled data, on the SemanticKITTI (59.5@5%) and
ScribbleKITTI (58.1@5%) benchmark datasets, based on a 2.3x reduction in model
parameters and 641x fewer multiply-add operations whilst also demonstrating
significant performance improvement on limited training data (i.e., Less is
More).

本文提出了一种基于稀疏深度可分离卷积模块的新型管道，该管道通过减少网络参数数量并保留整体任务性能，需要更少的地面真实注释来实现优越的分割准确性，并提出了一个新的用于处理 3D LiDAR 点云数据的样本下采样方法，与现有方法相比具有更好的性能，同时在有限的训练数据上也表现出显着的性能提高。