Affiliation:
1. School of Engineer, Beijing Forestry University, Beijing 100086, China
2. National Engineering Research Centre for Agri-product Quality Traceability, Beijing Technology and Business University, Beijing 100048, China
Abstract
The fast semantic segmentation algorithm of 3D laser point clouds for large scenes is of great significance for mobile information measurement systems, but the point cloud data is complex and generates problems such as disorder, rotational invariance, sparsity, severe occlusion, and unstructured data. We address the above problems by proposing the random sampling feature aggregation module ATSE module, which solves the problem of effective aggregation of features at different scales, and a new semantic segmentation framework PointLAE, which effectively presegments point clouds and obtains good semantic segmentation results by neural network training based on the features aggregated by the above module. We validate the accuracy of the algorithm by training on Semantic3D, a public dataset of large outdoor scenes, with an accuracy of 90.3, while verifying the robustness of the algorithm on Mvf CNN datasets with different sparsity levels, with an accuracy of 86.2, and on Bjfumap data aggregated by our own mobile environmental information collection platform, with an accuracy of 77.4, demonstrating that the algorithm is good for mobile information complex scale data in mobile information collection with great recognition effect.
Funder
Research, Development and Demonstration of Key Technologies for Restoration of Fluid Fixed Vegetation in Countries along the Silk Road
Subject
Computer Networks and Communications,Computer Science Applications
Cited by
4 articles.
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