Local structural feature description of point cloud by hierarchical projection

Shangtai Gu; Yanxin Ma; Ling Wang; Chao Ma

doi:10.1117/12.2577979

10 November 2020 Local structural feature description of point cloud by hierarchical projection

Shangtai Gu, Yanxin Ma, Ling Wang, Chao Ma

Proceedings Volume 11584, 2020 International Conference on Image, Video Processing and Artificial Intelligence; 1158411 (2020) https://doi.org/10.1117/12.2577979
Event: Third International Conference on Image, Video Processing and Artificial Intelligence, 2020, Shanghai, China

Abstract

This paper presents a local structural feature description of point cloud to efficiently extract local geometric and structure features from LIDAR data for 3-dimensional objective. This approach using hierarchical projection to maps neighbor points with different radial distance to multi-Mercator layers to obtain different distance information of neighbor points to key points. The Mercator projection, a conformal mapping method, the preserves geometric and structure relationship properly. The local features of key points can be obtained by calculating the distribution histogram of each Mercator planes with normalization method. Comparing the proposed approach with other hand-crafted feature extraction methods on Stanford Bologna dataset and 3Dmatch dataset, our methods outperform on descriptiveness, robustness to noise.

Citation Download Citation

Shangtai Gu, Yanxin Ma, Ling Wang, and Chao Ma "Local structural feature description of point cloud by hierarchical projection", Proc. SPIE 11584, 2020 International Conference on Image, Video Processing and Artificial Intelligence, 1158411 (10 November 2020); https://doi.org/10.1117/12.2577979

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