A. Gupta, Jonathan Byrne, D. Moloney, Hujun Yin, Simon Watson
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引用次数: 0
摘要
方法为了在激光雷达扫描中识别树木，首先使用渐进形态学滤波器对地面点进行识别和滤波。然后将过滤后的扫描体素化为稀疏的3D分层数据结构VOLA (Byrne et al.， 2017)，以降低输入分辨率。每体素2位的方法用于编码额外的信息，如颜色、强度和返回信息的数量。
3D Convolutional Neural Networks for Tree Detection using Automatically Annotated LiDAR data
Methods In order to identify trees in LiDAR scans, ground points are first identified and filtered using a Progressive Morphological Filter. This filtered scan is then voxelized in a sparse 3D hierarchical data structure, VOLA (Byrne et al., 2017), in order to reduce the input resolution. A 2 bits per voxel approach is used to encode additional information such as colour, intensity and number of returns information.
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