Results 81 to 90 of about 381,180 (306)

PCCA: 3D LiDAR road point cloud completion based on OSM boundary constrain

Measurement + Control
This paper proposes a novel LiDAR road point cloud completion algorithm (PCCA) to address the challenges in road point cloud completion due to factors such as sampling frequency and 3D object occlusion.
Xuefei Li, Linyao Qiu, Zhaoyang Ma
doaj   +1 more source

3D ShapeNets: A Deep Representation for Volumetric Shapes

, 2015
3D shape is a crucial but heavily underutilized cue in today's computer vision systems, mostly due to the lack of a good generic shape representation. With the recent availability of inexpensive 2.5D depth sensors (e.g.
Khosla, Aditya, Song, Shuran, Tang, Xiaoou, Wu, Zhirong, Xiao, Jianxiong, Yu, Fisher, Zhang, Linguang   +6 more
core   +1 more source

Self-Supervised Point Cloud Completion via Inpainting

Proceedings of the British Machine Vision Conference 2021, 2021
BMVC 2021 (Oral)
Mittal, Himangi, Okorn, Brian, Jangid, Arpit, Held, David   +3 more
openaire   +2 more sources

An Experimental High‐Throughput Approach for the Screening of Hard Magnet Materials

Advanced Engineering Materials, EarlyView.
An entire workflow for the high‐throughput characterization and analysis of compositionally graded magnetic films is presented. Characterization protocols, data management tools and data analysis approaches are illustrated with test case Sm(Fe, V)12 based films.
William Rigaut, Pierre Le Berre, Hamdi Jaballah, Lukas Fink, Victor Poline, Richard Haettel, Stéphane Grenier, Gilbert Chahine, Ewen Bellec, Guillaume Buatier, Wilfried Hortschitz, Santa Pile, Thomas Schrefl, Nils Blanc, Yuan Hong, Thibaut Devillers, Nora M. Dempsey   +16 more
wiley   +1 more source

Missing Region Completion Network for Large-Scale Laser-Scanned Point Clouds: Application to Transparent Visualization of Cultural Heritage

Remote Sensing
The digital documentation and analysis of cultural heritage increasingly rely on high-precision three-dimensional point cloud data, which often suffers from missing regions due to limitations in acquisition conditions, hindering subsequent analyses and ...
Weite Li, Jiao Pan, Kyoko Hasegawa, Liang Li, Satoshi Tanaka   +4 more
doaj   +1 more source

A Taxonomy for Management and Optimization of Multiple Resources in Edge Computing

, 2018
Edge computing is promoted to meet increasing performance needs of data-driven services using computational and storage resources close to the end devices, at the edge of the current network.
Nadjm-Tehrani, Simin, Toczé, Klervie
core   +1 more source

Enhancing Low‐Temperature Performance of Sodium‐Ion Batteries via Anion‐Solvent Interactions

Advanced Functional Materials, EarlyView.
DOL is introduced into electrolytes as a co‐solvent, increasing slat solubility, ion conductivity, and the de‐solvent process, and forming an anion‐rich solvent shell due to its high interaction with anion. With the above virtues, the batteries using this electrolyte exhibit excellent cycling stability at low temperatures. Abstract Sodium‐ion batteries
Cheng Zheng, Qian Yao, Yanan Sun, Hongyu Lv, Zhongchao Bai, Guoliang Zhang, Nana Wang, Jian Yang   +7 more
wiley   +1 more source

Deep Depth Completion of a Single RGB-D Image

, 2018
The goal of our work is to complete the depth channel of an RGB-D image. Commodity-grade depth cameras often fail to sense depth for shiny, bright, transparent, and distant surfaces.
Funkhouser, Thomas, Zhang, Yinda
core   +1 more source

Learning Local Displacements for Point Cloud Completion

2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2022
We propose a novel approach aimed at object and semantic scene completion from a partial scan represented as a 3D point cloud. Our architecture relies on three novel layers that are used successively within an encoder-decoder structure and specifically developed for the task at hand.
Wang, Yida, Tan, David Joseph, Navab, Nassir, Tombari, Federico   +3 more
openaire   +2 more sources

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