As the exorbitant expense of labeling autopilot datasets and the growing
trend of utilizing unlabeled data, semi-supervised segmentation on point clouds
becomes increasingly imperative. Intuitively, finding out more ``unspoken
words'' (i.e., latent instance information) beyond the label itself should be
helpful to improve performance. In this paper, we discover two types of latent
labels behind the displayed label embedded in LiDAR and image data. First, in
the LiDAR Branch, we propose a novel augmentation, Cylinder-Mix, which is able
to augment more yet reliable samples for training. Second, in the Image Branch,
we propose the Instance Position-scale Learning (IPSL) Module to learn and fuse
the information of instance position and scale, which is from a 2D pre-trained
detector and a type of latent label obtained from 3D to 2D projection. Finally,
the two latent labels are embedded into the multi-modal panoptic segmentation
network. The ablation of the IPSL module demonstrates its robust adaptability,
and the experiments evaluated on SemanticKITTI and nuScenes demonstrate that
our model outperforms the state-of-the-art method, LaserMix.

本文提出了两种潜在标签：LDAR Branch 中的新型数据增强方法 Cylinder-Mix，以及 Image Branch 中的 Instance Position-scale Learning (IPSL) 模块，并将这两个潜在标签嵌入多模态全景分割网络，实验证明我们的模型在 SemanticKITTI 和 nuScenes 上的表现优于最先进的方法 LaserMix。

标签本身之外：潜在标签强化半监督点云全景分割

Beyond the Label Itself: Latent Labels Enhance Semi-supervised Point  Cloud Panoptic Segmentation

Label hierarchy is an important source of external knowledge that can enhance
classification performance. However, most existing methods rely on predefined
label hierarchies that may not match the data distribution. To address this
issue, we propose Simultaneous label hierarchy Exploration And Learning (SEAL),
a new framework that explores the label hierarchy by augmenting the observed
labels with latent labels that follow a prior hierarchical structure. Our
approach uses a 1-Wasserstein metric over the tree metric space as an objective
function, which enables us to simultaneously learn a data-driven label
hierarchy and perform (semi-)supervised learning. We evaluate our method on
several datasets and show that it achieves superior results in both supervised
and semi-supervised scenarios and reveals insightful label structures. Our
implementation is available at this https URL

提出了一种名为 SEAL 的框架，该框架探索了标签层次结构，通过扩充观察到的标签与先前的分层结构的潜在标签进行了学习，并使用 1-Wasserstein 度量来同时执行数据驱动标签分层和（半）监督学习， 在几个数据集上进行了验证，结果表明该方法在监督和半监督场景下都取得了优异的成果，并揭示了有启发性的标签结构。