scholarly journals Estimating vertical canopy cover using dense image-based point cloud data in four vegetation types in southern Sweden

2017 ◽  
Vol 38 (7) ◽  
pp. 1820-1838 ◽  
Author(s):  
Ann-Helen Granholm ◽  
Nils Lindgren ◽  
Kenneth Olofsson ◽  
Mattias Nyström ◽  
Anna Allard ◽  
...  
Keyword(s):  
Point Cloud ◽  
Canopy Cover ◽  
Point Cloud Data ◽  
Vegetation Types ◽  
Southern Sweden ◽  
Cloud Data ◽  
Dense Image

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.

REPRESENTATION AND MODELING OF THREE-DIMENSIONAL LANDFORM DATA BASED ON POINT CLOUD DATA USING VARIOUS MEASUREMENT HARDWARE

2018 ◽  
Vol 74 (2) ◽  
pp. II_99-II_109 ◽  
Author(s):  
Satoshi KUBOTA ◽  
Ryuichi IMAI ◽  
Kenji NAKAMURA ◽  
Jun SAKURAI ◽  
Shigenori TANAKA
Keyword(s):  
Point Cloud ◽  
Three Dimensional ◽  
Point Cloud Data ◽  
Cloud Data

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

scholarly journals Direct rapid prototyping from point cloud data without surface reconstruction

2017 ◽  
Vol 15 (3) ◽  
pp. 390-398
Author(s):  
Tianyun Yuan ◽  
Xiaobo Peng ◽  
Dongdong Zhang
Keyword(s):  
Rapid Prototyping ◽  
Surface Reconstruction ◽  
Point Cloud ◽  
Point Cloud Data ◽  
Cloud Data

scholarly journals Seeing under the cover with a 3D U-Net: point cloud-based weight estimation of covered patients

2021 ◽  
Author(s):  
Alexander Bigalke ◽  
Lasse Hansen ◽  
Jasper Diesel ◽  
Mattias P. Heinrich
Keyword(s):  
Point Cloud ◽  
Drug Dosage ◽  
Sensor Data ◽  
Patient Specific ◽  
Point Cloud Data ◽  
Weight Estimation ◽  
Visual Sensor ◽  
Cloud Data ◽  
Emergency Situations ◽  
3D Cnn

Abstract Purpose Body weight is a crucial parameter for patient-specific treatments, particularly in the context of proper drug dosage. Contactless weight estimation from visual sensor data constitutes a promising approach to overcome challenges arising in emergency situations. Machine learning-based methods have recently been shown to perform accurate weight estimation from point cloud data. The proposed methods, however, are designed for controlled conditions in terms of visibility and position of the patient, which limits their practical applicability. In this work, we aim to decouple accurate weight estimation from such specific conditions by predicting the weight of covered patients from voxelized point cloud data. Methods We propose a novel deep learning framework, which comprises two 3D CNN modules solving the given task in two separate steps. First, we train a 3D U-Net to virtually uncover the patient, i.e. to predict the patient’s volumetric surface without a cover. Second, the patient’s weight is predicted from this 3D volume by means of a 3D CNN architecture, which we optimized for weight regression. Results We evaluate our approach on a lying pose dataset (SLP) under two different cover conditions. The proposed framework considerably improves on the baseline model by up to $${16}{\%}$$ 16 % and reduces the gap between the accuracy of weight estimates for covered and uncovered patients by up to $${52}{\%}$$ 52 % . Conclusion We present a novel pipeline to estimate the weight of patients, which are covered by a blanket. Our approach relaxes the specific conditions that were required for accurate weight estimates by previous contactless methods and thus constitutes an important step towards fully automatic weight estimation in clinical practice.

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