Classification and modeling of 3D point clouds in offshore waters based on color and image guidance

doi:10.13474/j.cnki.11-2246.2024.1120

Abstract

Abstract: In order to solve the problems of accuracy loss and blurring the boundaries between sea surface and mudflat in coastal sea features classification, we proposes a 3D point cloud classification and modeling method in offshore waters through color and image-guidance strategies. This method first uses the color distribution histogram to guide the rough classification of ground objects, and then uses SVM to achieve fine classification of ground objects. The 2D classification results will then be optimally mapped to the 3D point cloud, and on this basis, 3D model reconstruction of islands, mudflats and other features is carried out. The experimental result indicated the proposed method can accurately and reliably classify offshore sea features and achieve 3D model reconstruction of surface features.

Key words: point cloud classification, 3D reconstruction, coastal zone, island, mudflat

CLC Number:

P237

YU Rui, ZHANG Huiran, LIAO Jianbo. Classification and modeling of 3D point clouds in offshore waters based on color and image guidance[J]. Bulletin of Surveying and Mapping, 2024, 0(11): 115-119.

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