Zobrazeno 1 - 10
of 52
pro vyhledávání: '"Ji, Chaonan"'
Autor:
Pellicer-Valero, Oscar J., Fernández-Torres, Miguel-Ángel, Ji, Chaonan, Mahecha, Miguel D., Camps-Valls, Gustau
With climate change-related extreme events on the rise, high dimensional Earth observation data presents a unique opportunity for forecasting and understanding impacts on ecosystems. This is, however, impeded by the complexity of processing, visualiz
Externí odkaz:
http://arxiv.org/abs/2410.01770
Autor:
Montero, David, Kraemer, Guido, Anghelea, Anca, Aybar, César, Brandt, Gunnar, Camps-Valls, Gustau, Cremer, Felix, Flik, Ida, Gans, Fabian, Habershon, Sarah, Ji, Chaonan, Kattenborn, Teja, Martínez-Ferrer, Laura, Martinuzzi, Francesco, Reinhardt, Martin, Söchting, Maximilian, Teber, Khalil, Mahecha, Miguel D.
Recent advancements in Earth system science have been marked by the exponential increase in the availability of diverse, multivariate datasets characterised by moderate to high spatio-temporal resolutions. Earth System Data Cubes (ESDCs) have emerged
Externí odkaz:
http://arxiv.org/abs/2408.02348
Autor:
Ji, Chaonan, Fincke, Tonio, Benson, Vitus, Camps-Valls, Gustau, Fernandez-Torres, Miguel-Angel, Gans, Fabian, Kraemer, Guido, Martinuzzi, Francesco, Montero, David, Mora, Karin, Pellicer-Valero, Oscar J., Robin, Claire, Soechting, Maximilian, Weynants, Melanie, Mahecha, Miguel D.
With climate extremes' rising frequency and intensity, robust analytical tools are crucial to predict their impacts on terrestrial ecosystems. Machine learning techniques show promise but require well-structured, high-quality, and curated analysis-re
Externí odkaz:
http://arxiv.org/abs/2406.18179
Autor:
Montero, David, Aybar, César, Ji, Chaonan, Kraemer, Guido, Söchting, Maximilian, Teber, Khalil, Mahecha, Miguel D.
Advancements in Earth system science have seen a surge in diverse datasets. Earth System Data Cubes (ESDCs) have been introduced to efficiently handle this influx of high-dimensional data. ESDCs offer a structured, intuitive framework for data analys
Externí odkaz:
http://arxiv.org/abs/2404.13105
Autor:
Ji, Chaonan
Die Kartierung der städtische Oberflächenmaterialien ist aufgrund der komplexen räumlichen Muster eine Herausforderung. Daten von bildgebenden Spektrometern können hierbei durch die feine und kontinuierliche Abtastung des elektromagnetischen Spek
Externí odkaz:
http://edoc.hu-berlin.de/18452/25717
Single-image human relighting aims to relight a target human under new lighting conditions by decomposing the input image into albedo, shape and lighting. Although plausible relighting results can be achieved, previous methods suffer from both the en
Externí odkaz:
http://arxiv.org/abs/2207.04750
To address the ill-posed problem caused by partial observations in monocular human volumetric capture, we present AvatarCap, a novel framework that introduces animatable avatars into the capture pipeline for high-fidelity reconstruction in both visib
Externí odkaz:
http://arxiv.org/abs/2207.02031
Autor:
Zhao, Xiaochen, Zheng, Zerong, Ji, Chaonan, Liu, Zhenyi, Lin, Siyou, Yu, Tao, Suo, Jinli, Liu, Yebin
We introduce VERTEX, an effective solution to recover 3D shape and intrinsic texture of vehicles from uncalibrated monocular input in real-world street environments. To fully utilize the template prior of vehicles, we propose a novel geometry and tex
Externí odkaz:
http://arxiv.org/abs/2011.14642
Autor:
Hu, Zhongyang, Kuipers Munneke, Peter, Lhermitte, Stef, Dirscherl, Mariel, Ji, Chaonan, van den Broeke, Michiel
Publikováno v:
In Remote Sensing of Environment October 2022 280
Autor:
Ji, Chaonan, Bachmann, Martin, Esch, Thomas, Feilhauer, Hannes, Heiden, Uta, Heldens, Wieke, Hueni, Andreas, Lakes, Tobia, Metz-Marconcini, Annekatrin, Schroedter-Homscheidt, Marion, Weyand, Susanne, Zeidler, Julian
Publikováno v:
In Remote Sensing of Environment 1 December 2021 266