点云场景认知模式——泛化点云

Abstract

随着传感器技术和观测平台的迅速发展, 点云大数据作为新型遥感的主要数据形式, 已经逐渐成为场景感知的重要信息载体, 并在地质灾害态势感知、自然资源定量调查和道路交通安全服务等国家重大战略需求中发挥了越来越显著的作用。与此同时, 点云观测装备和国家重大战略需求的双重驱动促使空间场景从感知迈向了认知, 也对认知处理的算法和算力提出了新的要求。为此, 本文以点云场景认知的基本框架为线索, 分析了多源点云耦合观测的研究现状, 总结了点云场景认知处理的关键进展及其在国家重大战略需求中的典型应用, 并凝练了点云场景认知当前面临的主要问题。在此基础上, 本文聚焦点云场景认知的前沿挑战, 避开传统欧氏空间而转到高维张量流形空间进行点云数据处理, 提出了“泛化点云”的科学概念和技术框架, 为突破点云场景认知处理的算法和算力提供研究思路。

Alternate abstract:

With the rapid development of sensor technology and observation platform, point cloud data that is viewed as primary data of remote sensing, has gradually become an important information carrier. Moreover, it plays an increasingly significant role in the national major strategic needs such as geological disaster situation awareness, natural resources quantitative investigation and road traffic safety services. At the same time, driven by point cloud observation equipment and national major strategic needs, spatial scenes have changed from perception to cognition, and new requirements for cognitive processing algorithms and computing power have also been put forward. Therefore, based on the basic framework of point cloud scene cognition, this paper analyzes the research status of multi-source point cloud coupled observation, summarizes the key progress of point cloud scene cognition and typical applications in major national strategic needs, and summarizes the main problems facing point cloud scene cognition at present. On this basis, this paper focuses on the cutting-edge challenges of cloud scene cognition, avoids the traditional Euclidean space and turns to the high-dimensional tensor manifold space for point cloud data processing, proposes the scientific concept and technical framework of generalized point cloud, and provides a new research idea for the algorithm and computing power of cognitive processing of point cloud scene.