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).

翻译：尽管近年来3D激光雷达点云数据的可用性显著增长，但其标注仍成本高昂且耗时，导致在自动驾驶等应用领域中对半监督语义分割方法的需求日益迫切。现有工作常采用较大的分割骨干网络以提升分割精度，但代价是计算成本增加。此外，许多方法使用均匀采样来减少学习所需地面真值数据，通常导致次优性能。为解决这些问题，我们提出一种新流程：采用更小规模的架构，在所需地面真值标注更少的情况下，实现优于现有方法的分割精度。这一成果得益于新型稀疏深度可分离卷积模块，该模块在保持整体任务性能的同时大幅减少网络参数量。为有效对训练数据进行子采样，我们提出全新时空冗余帧下采样方法（ST-RFD），该方法利用传感器在环境中的运动知识来提取更具多样性的训练数据帧样本。为进一步利用有限标注数据样本，我们提出基于激光雷达反射率的软伪标签方法。在模型参数量减少2.3倍、乘加操作减少641倍的条件下，本方法在SemanticKITTI（59.5@5%）和ScribbleKITTI（58.1@5%）基准数据集上以更少标注数据实现了优于现有半监督工作的mIoU，同时在有限训练数据下展现出显著性能提升（即更少即是更多）。