Scene cognition pattern of point cloud-generalization point cloud

doi:10.11947/j.AGCS.2022.20220019

Abstract

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.

Key words: point cloud data, associative cognition, intelligent processing, generalized point cloud, high-dimensional tensor manifold space

CLC Number:

P237

LIU Chun, JIA Shoujun, WU Hangbin, HUANG Wei, ZHENG Ning, AKRAM Akbar. Scene cognition pattern of point cloud-generalization point cloud[J]. Acta Geodaetica et Cartographica Sinica, 2022, 51(4): 556-567.

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