Parallel Globally Consistent Normal Orientation of Raw Unorganized Point Clouds

2019 ◽  
Vol 38 (5) ◽  
pp. 163-173 ◽  
Author(s):  
J. Jakob ◽  
C. Buchenau ◽  
M. Guthe
Keyword(s):  
Point Clouds ◽  
Normal Orientation

scholarly journals Automatic Normal Orientation in Point Clouds of Building Interiors

2019 ◽  
pp. 556-563 ◽  
Author(s):  
Sebastian Ochmann ◽  
Reinhard Klein
Keyword(s):  
Point Clouds ◽  
Normal Orientation

Fuzzy logic and histogram of normal orientation-based 3D keypoint detection for point clouds

2020 ◽  
Vol 136 ◽  
pp. 40-47
Author(s):  
Muhammad Zafar Iqbal ◽  
Dmytro Bobkov ◽  
Eckehard Steinbach
Keyword(s):  
Fuzzy Logic ◽  
Point Clouds ◽  
Keypoint Detection ◽  
Normal Orientation ◽  
3D Keypoint Detection

scholarly journals AUTOMATIC MODELLING OF RUBBLE MOUND BREAKWATERS FROM LIDAR DATA

2015 ◽  
Vol XL-3/W3 ◽  
pp. 9-13 ◽  
Author(s):  
M. Bueno ◽  
L. Díaz-Vilariño ◽  
H. González-Jorge ◽  
J. Martínez-Sánchez ◽  
P. Arias
Keyword(s):  
Collision Detection ◽  
Point Cloud ◽  
Accuracy Assessment ◽  
Computing Time ◽  
Point Clouds ◽  
Edge Length ◽  
Normal Vector ◽  
Normal Orientation ◽  
Rubble Mound ◽  
Rubble Mound Breakwaters

Rubble mound breakwaters maintenance is critical to the protection of beaches and ports. LiDAR systems provide accurate point clouds from the emerged part of the structure that can be modelled to make it more useful and easy to handle. This work introduces a methodology for the automatic modelling of breakwaters with armour units of cube shape. The algorithm is divided in three main steps: normal vector computation, plane segmentation, and cube reconstruction. Plane segmentation uses the normal orientation of the points and the edge length of the cube. Cube reconstruction uses the intersection of three perpendicular planes and the edge length. Three point clouds cropped from the main point cloud of the structure are used for the tests. The number of cubes detected is around 56 % for two of the point clouds and 32 % for the third one over the total physical cubes. Accuracy assessment is done by comparison with manually drawn cubes calculating the differences between the vertexes. It ranges between 6.4 cm and 15 cm. Computing time ranges between 578.5 s and 8018.2 s. The computing time increases with the number of cubes and the requirements of collision detection.

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.

Topological feature recognition and blend feature protection for non-duality point clouds

2020 ◽  
Vol 28 (10) ◽  
pp. 2301-2310
Author(s):  
Chun-kang ZHANG ◽  
◽  
Hong-mei LI ◽  
Xia ZHANG
Keyword(s):  
Feature Recognition ◽  
Point Clouds ◽  
Topological Feature

Automatic breakline extraction from LIDAR point clouds

2008 ◽  
Vol 28 (5) ◽  
pp. 1214-1216 ◽  
Author(s):  
Jing-zhong XU
Keyword(s):  
Point Clouds

Workspace Modeling: Visualization and Pose Estimation of Teleoperated Construction Equipment from Point Clouds

2020 ◽  
Author(s):  
Jing Dao Chen ◽  
Pileun Kim ◽  
Dong-Ik Sun ◽  
Chang-Soo Han ◽  
Yong Han Ahn ◽  
...  
Keyword(s):  
Pose Estimation ◽  
Point Clouds ◽  
Construction Equipment

USING POINT CLOUDS TO EVALUATE SLOPE PERFORMANCE WITH RESPECT TO ROCKFALL GENERATION

2018 ◽  
Author(s):  
Marissa J. Dudek ◽  
◽  
John Paul Ligush ◽  
Colin Hogg ◽  
Yonathan Admassu
Keyword(s):  
Point Clouds

scholarly journals Disturbance Analysis in the Classification of Objects Obtained from Urban LiDAR Point Clouds with Convolutional Neural Networks

2021 ◽  
Vol 13 (11) ◽  
pp. 2135
Author(s):  
Jesús Balado ◽  
Pedro Arias ◽  
Henrique Lorenzo ◽  
Adrián Meijide-Rodríguez
Keyword(s):  
Laser Scanning ◽  
Ambient Noise ◽  
Point Clouds ◽  
Density Variation ◽  
Density Reduction ◽  
Manual Intervention ◽  
Point Density ◽  
Disturbance Analysis ◽  
Street Furniture

Mobile Laser Scanning (MLS) systems have proven their usefulness in the rapid and accurate acquisition of the urban environment. From the generated point clouds, street furniture can be extracted and classified without manual intervention. However, this process of acquisition and classification is not error-free, caused mainly by disturbances. This paper analyses the effect of three disturbances (point density variation, ambient noise, and occlusions) on the classification of urban objects in point clouds. From point clouds acquired in real case studies, synthetic disturbances are generated and added. The point density reduction is generated by downsampling in a voxel-wise distribution. The ambient noise is generated as random points within the bounding box of the object, and the occlusion is generated by eliminating points contained in a sphere. Samples with disturbances are classified by a pre-trained Convolutional Neural Network (CNN). The results showed different behaviours for each disturbance: density reduction affected objects depending on the object shape and dimensions, ambient noise depending on the volume of the object, while occlusions depended on their size and location. Finally, the CNN was re-trained with a percentage of synthetic samples with disturbances. An improvement in the performance of 10–40% was reported except for occlusions with a radius larger than 1 m.

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