Curvature and density based feature point detection for point cloud data

2010 ◽  
Author(s):  
Lihui Wang ◽  
Baozong Yuan
Keyword(s):  
Point Cloud ◽  
Feature Point ◽  
Point Cloud Data ◽  
Cloud Data ◽  
Point Detection

The Filtering and Streamline of Three-Dimensional Point-Cloud Data

2014 ◽  
Vol 511-512 ◽  
pp. 554-558 ◽  
Author(s):  
Zheng Chang Zhang
Keyword(s):  
Point Cloud ◽  
Three Dimensional ◽  
Feature Point ◽  
Wiener Filtering ◽  
Point Cloud Data ◽  
Cloud Data ◽  
Gray Image ◽  
Noise Point ◽  
Reconstructed Surfaces ◽  
Z Value

Three-dimensional scanning device will scan a large number of three-dimensional data one time, which will inevitably mixed with some of the noise points, casusing the reconstructed surfaces and curves that is not smooth. At the same time a large number of three-dimensional data can lead to reconstructing surface slow down. This paper applied Wiener-filtering which is commonly used in the gray image de-noising and smoothing treatment to filtering three-dimensional point-cloud-data by replace the gray value of gray image with a z value of point-cloud-data, and the point-cloud-data which undulates strongly will be seen as noise point and removed. At the same time using octree algorithm to streamline the data, which can be guaranteed to retain local feature point cloud data while streamlining data.

Registration and Fusion of UAV LiDAR System Sequence Images and Laser Point Clouds

2020 ◽  
Author(s):  
Jiayong Yu ◽  
Longchen Ma ◽  
Maoyi Tian, ◽  
Xiushan Lu
Keyword(s):  
Point Cloud ◽  
Image Data ◽  
Point Clouds ◽  
Optical Image ◽  
Point Cloud Data ◽  
Feature Points ◽  
Lidar System ◽  
Registration Accuracy ◽  
Cloud Data ◽  
Intensity Image

The unmanned aerial vehicle (UAV)-mounted mobile LiDAR system (ULS) is widely used for geomatics owing to its efficient data acquisition and convenient operation. However, due to limited carrying capacity of a UAV, sensors integrated in the ULS should be small and lightweight, which results in decrease in the density of the collected scanning points. This affects registration between image data and point cloud data. To address this issue, the authors propose a method for registering and fusing ULS sequence images and laser point clouds, wherein they convert the problem of registering point cloud data and image data into a problem of matching feature points between the two images. First, a point cloud is selected to produce an intensity image. Subsequently, the corresponding feature points of the intensity image and the optical image are matched, and exterior orientation parameters are solved using a collinear equation based on image position and orientation. Finally, the sequence images are fused with the laser point cloud, based on the Global Navigation Satellite System (GNSS) time index of the optical image, to generate a true color point cloud. The experimental results show the higher registration accuracy and fusion speed of the proposed method, thereby demonstrating its accuracy and effectiveness.

ESTIMATION OF VEGETATION DENSITY OF TREES USING ALB POINT CLOUD DATA

2018 ◽  
Vol 74 (4) ◽  
pp. I_547-I_552
Author(s):  
Keisuke YOSHIDA ◽  
Shiro MAENO ◽  
Syuhei OGAWA ◽  
Sadayuki ISEKI ◽  
Ryosuke AKOH
Keyword(s):  
Point Cloud ◽  
Point Cloud Data ◽  
Vegetation Density ◽  
Cloud Data

3D Modeling Approach of Building Construction based on Point Cloud Data Using LiDAR

2019 ◽  
Author(s):  
Byeongjun Oh ◽  
Minju Kim ◽  
Chanwoo Lee ◽  
Hunhee Cho ◽  
Kyung-In Kang
Keyword(s):  
3D Modeling ◽  
Point Cloud ◽  
Building Construction ◽  
Point Cloud Data ◽  
Cloud Data ◽  
Modeling Approach
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