Structuralization of LiDAR Point Cloud by Combining 3-D Grid Structure and Iterated Hough Transform
Date Issued
2005
Date
2005
Author(s)
Huang, Ku-Yen
DOI
zh-TW
Abstract
LiDAR systems have recently emerged as efficient tools for
collecting geo-spatial information. Due to the discrete nature of point
cloud, regardless of ground-based or airborne LiDAR systems, series of
processes via designed algorithms and aids from the computer power on
the data must be imposed before any features or information can be
revealed.
This research employs 3-D grid structure well addressing point
cloud into 3-D topology, regional growing for hypothesizing planes, and
iterated Hough Transform for refining the plane-features. Line features
and point features can then be derived based on the previous solution on
plane extraction. The experimental results show that the proposed
structuralization scheme is applicable and the extracted features are to be
satisfactorily utilized for relevant applications.
Subjects
三維網格
霍夫轉換
結構化
特徵萃取
誤差傳播
3-D Grid
Hough Transform
Perceptual Organization
Feature Extraction
Error Propagation
SDGs
Type
thesis
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