Results 71 to 80 of about 165,702 (282)

Ground Extraction from 3D Lidar Point Clouds [PDF]

, 2018
© 2018 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new ...
Mandow, Anthony, Martinez-Sanchez, Maria Alcazar, Martínez, Jorge L., Morán, Mariano, Pomares, Antonio   +4 more
core  

Towards Defect Phase Diagrams: From Research Data Management to Automated Workflows

Advanced Engineering Materials, EarlyView.
A research data management infrastructure is presented for the systematic integration of heterogeneous experimental and simulation data required for defect phase diagrams. The approach combines openBIS with a companion application for large‐object storage, automated metadata extraction, provenance tracking and federated data access, thereby supporting ...
Khalil Rejiba, Sang‐Hyeok Lee, Christina Gasper, Martina Freund, Sandra Korte‐Kerzel, Ulrich Kerzel   +5 more
wiley   +1 more source

Unleashing the Power of Machine Learning in Nanomedicine Formulation Development

Advanced Functional Materials, EarlyView.
A random forest machine learning model is able to make predictions on nanoparticle attributes of different nanomedicines (i.e. lipid nanoparticles, liposomes, or PLGA nanoparticles) based on microfluidic formulation parameters. Machine learning models are based on a database of nanoparticle formulations, and models are able to generate unique solutions
Thomas L. Moore, Cristiano Pesce, Antonietta Greco, Claudia Pisante, Greta Avancini, Valentina Di Francesco, Yosi Shamay, Paolo Decuzzi   +7 more
wiley   +1 more source

Evaluation of Input Sampling Methods for Deep-Learning-Based Semantic Segmentation of Large-Scale 3D Point Clouds [PDF]

ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences
3D point clouds used in geospatial applications typically contain billions of points. Processing 3D point clouds of this size as a whole with deep learning models requires computational resources (e.g., GPU memory) that are usually not available.
J. F. Ciprián-Sánchez, J.-M. Burmeister, R. Richter, G. Ochoa Ruiz, J. Döllner   +4 more
doaj   +1 more source

3D CITY MODELLING OF ISTANBUL BASED ON LIDAR DATA AND PANORAMIC IMAGES – ISSUES AND CHALLENGES [PDF]

The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, 2019
This paper describes the generation of 3D city modelling of LoD2 and LoD3 buildings based on 3D point clouds data and other auxiliary data for Istanbul city, Turkey.
G. Buyuksalih, P. Baskaraca, S. Bayburt, I. Buyuksalih, A. Abdul Rahman   +4 more
doaj   +1 more source

A Prior Level Fusion Approach for the Semantic Segmentation of 3D Point Clouds Using Deep Learning

Remote Sensing, 2022
Three-dimensional digital models play a pivotal role in city planning, monitoring, and sustainable management of smart and Digital Twin Cities (DTCs). In this context, semantic segmentation of airborne 3D point clouds is crucial for modeling, simulating,
Zouhair Ballouch, Rafika Hajji, Florent Poux, Abderrazzaq Kharroubi, Roland Billen   +4 more
doaj   +1 more source

Shape Generation using Spatially Partitioned Point Clouds

, 2017
We propose a method to generate 3D shapes using point clouds. Given a point-cloud representation of a 3D shape, our method builds a kd-tree to spatially partition the points.
Gadelha, Matheus, Maji, Subhransu, Wang, Rui   +2 more
core   +1 more source

Shaping Ti3C2 MXene Nanospheres for Precision Near‐Infrared Photothermal Therapy

Advanced Functional Materials, EarlyView.
In this study, we report producing spherical MXenes via fs laser fragmentation of Ti3C2 flakes in liquid medium. The nanoparticles demonstrated pronounced light absorption and high photothermal conversion efficiencies of 68% and 63% under heating with NIR‐I and NIR‐II lasers, respectively.
Julia S. Babkova, Ivan V. Zelepukin, Konstantin G. Shevchenko, Gleb V. Tikhonowski, Lyubov V. Gorelik, Anton A. Popov, Anna S. Sogomonyan, Alina Yu. Kapitannikova, Ivan S. Kazantsev, Dmitry V. Dyubo, Daria A. Panova, Daniil I. Tselikov, Anton A. Minnekhanov, Aziz B. Mirkasymov, Georgy A. Ermolaev, Alexander V. Syuy, Gleb I. Tselikov, Aleksey V. Arsenin, Andrei V. Kabashin, Kostya S. Novoselov, Sergey M. Deyev, Valentyn S. Volkov   +21 more
wiley   +1 more source

Efficient and lightweight 3D building reconstruction from drone imagery using sparse line and point clouds

Virtual Reality & Intelligent Hardware
Efficient three-dimensional (3D) building reconstruction from drone imagery often faces data acquisition, storage, and computational challenges because of its reliance on dense point clouds.
Xiongjie Yin, Jinquan He, Zhanglin Cheng
doaj   +1 more source

DENSE 3D POINT CLOUD GENERATION FROM UAV IMAGES FROM IMAGE MATCHING AND GLOBAL OPTIMAZATION [PDF]

The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, 2016
3D spatial information from unmanned aerial vehicles (UAV) images is usually provided in the form of 3D point clouds. For various UAV applications, it is important to generate dense 3D point clouds automatically from over the entire extent of UAV images.
S. Rhee, T. Kim
doaj   +1 more source