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Relevant bibliographies by topics / SfM and LiDAR

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Journal articles

Academic literature on the topic 'SfM and LiDAR'

Author: Grafiati

Published: 1 April 2023

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Journal articles on the topic "SfM and LiDAR"

1

Morgan, Carli J., Matthew Powers, and Bogdan M. Strimbu. "Estimating Tree Defects with Point Clouds Developed from Active and Passive Sensors." Remote Sensing 14, no. 8 (2022): 1938. http://dx.doi.org/10.3390/rs14081938.

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Traditional inventories require large investments of resources and a trained workforce to measure tree sizes and characteristics that affect wood quality and value, such as the presence of defects and damages. Handheld light detection and ranging (LiDAR) and photogrammetric point clouds developed using Structure from Motion (SfM) algorithms achieved promising results in tree detection and dimensional measurements. However, few studies have utilized handheld LiDAR or SfM to assess tree defects or damages. We used a Samsung Galaxy S7 smartphone camera to photograph trees and create digital model

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2

Obanawa, Hiroyuki, Rena Yoshitoshi, Nariyasu Watanabe, and Seiichi Sakanoue. "Portable LiDAR-Based Method for Improvement of Grass Height Measurement Accuracy: Comparison with SfM Methods." Sensors 20, no. 17 (2020): 4809. http://dx.doi.org/10.3390/s20174809.

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Plant height is a key indicator of grass growth. However, its accurate measurement at high spatial density with a conventional ruler is time-consuming and costly. We estimated grass height with high accuracy and speed using the structure from motion (SfM) and portable light detection and ranging (LiDAR) systems. The shapes of leaf tip surface and ground in grassland were determined by unmanned aerial vehicle (UAV)-SfM, pole camera-SfM, and hand-held LiDAR, before and after grass harvesting. Grass height was most accurately estimated using the difference between the maximum value of the point c

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3

Broxton, Patrick D., and Willem J. D. van Leeuwen. "Structure from Motion of Multi-Angle RPAS Imagery Complements Larger-Scale Airborne Lidar Data for Cost-Effective Snow Monitoring in Mountain Forests." Remote Sensing 12, no. 14 (2020): 2311. http://dx.doi.org/10.3390/rs12142311.

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Snowmelt from mountain forests is critically important for water resources and hydropower generation. More than 75% of surface water supply originates as snowmelt in mountainous regions, such as the western U.S. Remote sensing has the potential to measure snowpack in these areas accurately. In this research, we combine light detection and ranging (lidar) from crewed aircraft (currently, the most reliable way of measuring snow depth in mountain forests) and structure from motion (SfM) remotely piloted aircraft systems (RPAS) for cost-effective multi-temporal monitoring of snowpack in mountain f

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4

Zhang, Fei, Amirhossein Hassanzadeh, Julie Kikkert, Sarah Jane Pethybridge, and Jan van Aardt. "Comparison of UAS-Based Structure-from-Motion and LiDAR for Structural Characterization of Short Broadacre Crops." Remote Sensing 13, no. 19 (2021): 3975. http://dx.doi.org/10.3390/rs13193975.

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The use of small unmanned aerial system (UAS)-based structure-from-motion (SfM; photogrammetry) and LiDAR point clouds has been widely discussed in the remote sensing community. Here, we compared multiple aspects of the SfM and the LiDAR point clouds, collected concurrently in five UAS flights experimental fields of a short crop (snap bean), in order to explore how well the SfM approach performs compared with LiDAR for crop phenotyping. The main methods include calculating the cloud-to-mesh distance (C2M) maps between the preprocessed point clouds, as well as computing a multiscale model-to-mo

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5

Liao, Jianghua, Jinxing Zhou, and Wentao Yang. "Comparing LiDAR and SfM digital surface models for three land cover types." Open Geosciences 13, no. 1 (2021): 497–504. http://dx.doi.org/10.1515/geo-2020-0257.

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Abstract Airborne light detection and ranging (LiDAR) and unmanned aerial vehicle structure from motion (UAV-SfM) are two major methods used to produce digital surface models (DSMs) for geomorphological studies. Previous studies have used both types of DSM datasets interchangeably and ignored their differences, whereas others have attempted to locally compare these differences. However, few studies have quantified these differences for different land cover types. Therefore, we simultaneously compared the two DSMs using airborne LiDAR and UAV-SfM for three land cover types (i.e. forest, wastela

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6

Ratner, JJ, JJ Sury, MR James, TA Mather, and DM Pyle. "Crowd-sourcing structure-from- motion data for terrain modelling in a real-world disaster scenario: A proof of concept." Progress in Physical Geography: Earth and Environment 43, no. 2 (2019): 236–59. http://dx.doi.org/10.1177/0309133318823622.

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Structure-from-motion (SfM) photogrammetry techniques are now widely available to generate digital terrain models (DTMs) from optical imagery, providing an alternative to costlier options such as LiDAR or satellite surveys. SfM could be a useful tool in hazard studies because its minimal cost makes it accessible even in developing regions and its speed of use can provide updated data rapidly in hazard-prone regions. Our study is designed to assess whether crowd-sourced SfM data is comparable to an industry standard LiDAR dataset, demonstrating potential real-world use of SfM if employed for di

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7

Gassen, Fabian, Eberhard Hasche, Patrick Ingwer, and Reiner Creutzburg. "Supplementation of Lidar Scans with Structure from Motion (SfM) Data." Electronic Imaging 2016, no. 7 (2016): 1–6. http://dx.doi.org/10.2352/issn.2470-1173.2016.7.mobmu-297.

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8

Mikita, Tomáš, Marie Balková, Aleš Bajer, Miloš Cibulka, and Zdeněk Patočka. "Comparison of Different Remote Sensing Methods for 3D Modeling of Small Rock Outcrops." Sensors 20, no. 6 (2020): 1663. http://dx.doi.org/10.3390/s20061663.

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This paper reviews the use of modern 3D image-based and Light Detection and Ranging (LiDAR) methods of surface reconstruction techniques for high fidelity surveys of small rock outcrops to highlight their potential within structural geology and landscape protection. LiDAR and Structure from Motion (SfM) software provide useful opportunities for rock outcrops mapping and 3D model creation. The accuracy of these surface reconstructions is crucial for quantitative structural analysis. However, these technologies require either a costly data acquisition device (Terrestrial LiDAR) or specialized im

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9

Harder, Phillip, John W. Pomeroy, and Warren D. Helgason. "Improving sub-canopy snow depth mapping with unmanned aerial vehicles: lidar versus structure-from-motion techniques." Cryosphere 14, no. 6 (2020): 1919–35. http://dx.doi.org/10.5194/tc-14-1919-2020.

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Abstract. Vegetation has a tremendous influence on snow processes and snowpack dynamics, yet remote sensing techniques to resolve the spatial variability of sub-canopy snow depth are not always available and are difficult from space-based platforms. Unmanned aerial vehicles (UAVs) have had recent widespread application to capture high-resolution information on snow processes and are herein applied to the sub-canopy snow depth challenge. Previous demonstrations of snow depth mapping with UAV structure from motion (SfM) and airborne lidar have focussed on non-vegetated surfaces or reported large

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10

Nagy, Balázs, and Csaba Benedek. "On-the-Fly Camera and Lidar Calibration." Remote Sensing 12, no. 7 (2020): 1137. http://dx.doi.org/10.3390/rs12071137.

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Sensor fusion is one of the main challenges in self driving and robotics applications. In this paper we propose an automatic, online and target-less camera-Lidar extrinsic calibration approach. We adopt a structure from motion (SfM) method to generate 3D point clouds from the camera data which can be matched to the Lidar point clouds; thus, we address the extrinsic calibration problem as a registration task in the 3D domain. The core step of the approach is a two-stage transformation estimation: First, we introduce an object level coarse alignment algorithm operating in the Hough space to tran

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