3D 视觉

A Survey on Deep Learning Architectures for Point Cloud Classification and Segmentation

针对点云分类和分割的深度学习架构系统性调研

Minhas Kamal, Hiranya Garbha Kumar, Balakrishnan Prabhakaran

专题命中 点云 ：系统性调研点云分类和分割的深度学习架构。

AI总结 本文系统性地探讨了点云分类和分割中的深度学习架构，分析了点云数据的结构特性，分类了不同架构的工作，并评估了其在主流基准上的性能，同时指出了开放挑战和未来方向。

Comments We reviewed a decade of advancements in point cloud processing: trace the evolution of the field from its foundational roots to the modern SOTA, analyze how diverse architectures overcome the inherent geometric challenges of 3D data, and map out critical research gaps alongside promising future directions. GitHub: https://github.com/MinhasKamal/DeepLearningForPointCloud

Journal ref ACM Transactions on Multimedia Computing, Communications, and Applications (TOMM), 2026

Aerial-ground LiDAR place recognition with patch-level self-supervised learning and expanded reciprocal re-ranking

空地激光雷达地点识别：基于块级自监督学习和扩展互逆重排序

Yandi Yang, Xianghong Zou, Jianping Li, Haofeng Xie, Saurav Uprety, Hongzhou Yang, Naser El-Sheimy

专题命中 点云 ：空地激光雷达地点识别框架，点云检索重排序

AI总结 提出一种空地激光雷达地点识别框架，通过多尺度块级自监督学习缩小域差距，并利用扩展互逆重排序算法减少误检，在多个数据集上显著提升检索精度。

Objective Quality Assessment of Point Clouds Using Multi-scale Implicit Structural Similarity

点云的多尺度隐式结构相似性客观质量评估

Zhang Chen, Shuai Wan, Yuezhe Zhang, Siyu Ren, Fuzheng Yang, Junhui Hou

专题命中 点云 ：点云质量评估，多尺度隐式结构相似性

AI总结 针对点云质量评估中不规则数据匹配困难的问题，提出多尺度隐式结构相似性度量（MS-ISSM），通过径向基函数连续表示局部特征并比较隐式函数系数，结合ResGrouped-MLP网络，在多个基准上超越现有方法。

Comments IEEE TMM Accepted

C-ARC: Continuous-Adaptive Range Clustering for Non-Repetitive LiDAR Sensors

C-ARC: 面向非重复式LiDAR传感器的连续自适应范围聚类

Nick B. Schroeder, Jonathan Lichtenfeld, Oskar von Stryk

专题命中 点云 ：处理非重复式LiDAR点云聚类，属于3D视觉

AI总结 提出C-ARC框架，通过滑动窗口上的持久双图结构解耦高频点插入与按需聚类检索，并利用指数控制环自适应校准网格分辨率，实现非重复式LiDAR点云的实时聚类。

Comments Submitted to IEEE Robotics and Automation Letters. This work has been submitted to the IEEE for possible publication. 8 pages, 7 figures