Existing state-of-the-art 3D point clouds understanding methods only perform
well in a fully supervised manner. To the best of our knowledge, there exists
no unified framework which simultaneously solves the downstream high-level
understanding tasks, especially when labels are extremely limited. This work
presents a general and simple framework to tackle point clouds understanding
when labels are limited. We propose a novel unsupervised region expansion based
clustering method for generating clusters. More importantly, we innovatively
propose to learn to merge the over-divided clusters based on the local
low-level geometric property similarities and the learned high-level feature
similarities supervised by weak labels. Hence, the true weak labels guide
pseudo labels merging taking both geometric and semantic feature correlations
into consideration. Finally, the self-supervised reconstruction and data
augmentation optimization modules are proposed to guide the propagation of
labels among semantically similar points within a scene. Experimental Results
demonstrate that our framework has the best performance among the three most
important weakly supervised point clouds understanding tasks including semantic
segmentation, instance segmentation, and object detection even when limited
points are labeled, under the data-efficient settings for the large-scale 3D
semantic scene parsing. The developed techniques have postentials to be applied
to downstream tasks for better representations in robotic manipulation and
robotic autonomous navigation. Codes and models are publicly available at:
this https URL

本文介绍了一种在有限标签情况下解决点云理解问题的通用简单框架，通过无监督区域扩展的聚类方法生成聚类，并结合地理特征相似性和语义特征相似性在弱标签监督下学习合并过度细分的聚类，以及自监督重建和数据增强优化模块引导标签在场景中语义相似点之间的传播。实验证明，该框架在大规模 3D 语义场景解析的数据效率设置下，在语义分割、实例分割和目标检测等三个重要弱监督点云理解任务中表现最佳，所开发技术还具有对机器人操作和自主导航中的下游任务提供更好表示的潜力。代码和模型可在链接处公开获取。

一种用于机器人大规模 LiDAR 场景解析的高效框架

A Data-efficient Framework for Robotics Large-scale LiDAR Scene Parsing

Semantic scene parsing is suffering from the fact that pixel-level
annotations are hard to be collected. To tackle this issue, we propose a
Point-based Distance Metric Learning (PDML) in this paper. PDML does not
require dense annotated masks and only leverages several labeled points that
are much easier to obtain to guide the training process. Concretely, we
leverage semantic relationship among the annotated points by encouraging the
feature representations of the intra- and inter-category points to keep
consistent, i.e. points within the same category should have more similar
feature representations compared to those from different categories. We
formulate such a characteristic into a simple distance metric loss, which
collaborates with the point-wise cross-entropy loss to optimize the deep neural
networks. Furthermore, to fully exploit the limited annotations, distance
metric learning is conducted across different training images instead of simply
adopting an image-dependent manner. We conduct extensive experiments on two
challenging scene parsing benchmarks of PASCAL-Context and ADE 20K to validate
the effectiveness of our PDML, and competitive mIoU scores are achieved.

本文提出一种基于点的距离度量学习（PDML）方法，通过鼓励标注点之间的特征保持一致来引导训练过程，使用限定的标注点代替密集标注 mask 进行语义场景解析，最终在 PASCAL-Context 和 ADE 20K 数据集上实验验证结果表明 PDML 方法有效性较高。