In recent years, point cloud representation has become one of the research
hotspots in the field of computer vision, and has been widely used in many
fields, such as autonomous driving, virtual reality, robotics, etc. Although
deep learning techniques have achieved great success in processing regular
structured 2D grid image data, there are still great challenges in processing
irregular, unstructured point cloud data. Point cloud classification is the
basis of point cloud analysis, and many deep learning-based methods have been
widely used in this task. Therefore, the purpose of this paper is to provide
researchers in this field with the latest research progress and future trends.
First, we introduce point cloud acquisition, characteristics, and challenges.
Second, we review 3D data representations, storage formats, and commonly used
datasets for point cloud classification. We then summarize deep learning-based
methods for point cloud classification and complement recent research work.
Next, we compare and analyze the performance of the main methods. Finally, we
discuss some challenges and future directions for point cloud classification.

该论文介绍了点云的获取、特点和挑战，回顾了点云分类的 3D 数据表示、存储格式和常用数据集，总结了基于深度学习的点云分类方法，并对最新研究进展进行了补充，对主要方法的性能进行了比较和分析，并讨论了点云分类面临的一些挑战和未来方向。

基于深度学习的三维点云分类：系统调查与展望

Deep Learning-based 3D Point Cloud Classification: A Systematic Survey  and Outlook

Extraterrestrial rovers with a general-purpose robotic arm have many
potential applications in lunar and planetary exploration. Introducing autonomy
into such systems is desirable for increasing the time that rovers can spend
gathering scientific data and collecting samples. This work investigates the
applicability of deep reinforcement learning for vision-based robotic grasping
of objects on the Moon. A novel simulation environment with
procedurally-generated datasets is created to train agents under challenging
conditions in unstructured scenes with uneven terrain and harsh illumination. A
model-free off-policy actor-critic algorithm is then employed for end-to-end
learning of a policy that directly maps compact octree observations to
continuous actions in Cartesian space. Experimental evaluation indicates that
3D data representations enable more effective learning of manipulation skills
when compared to traditionally used image-based observations. Domain
randomization improves the generalization of learned policies to novel scenes
with previously unseen objects and different illumination conditions. To this
end, we demonstrate zero-shot sim-to-real transfer by evaluating trained agents
on a real robot in a Moon-analogue facility.

此项研究探讨了使用深度强化学习对月球上基于视觉的机械臂进行抓取操作的智能化控制，通过虚拟环境的数据训练及域随机化技术，实现零样本迁移并成功验证实际机器人在类月环境中的操作效果。