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cjdb: a simple, fast, and lean database solution for the CityGML data model

Autor: Powałka, Leon, Poon, Chris, Xia, Yitong, Meines, Siebren, Yan, Lan, Cai, Yuduan, Stavropoulou, Gina, Dukai, Balázs, Ledoux, Hugo

When it comes to storing 3D city models in a database, the implementation of the CityGML data model can be quite demanding and often results in complicated schemas. As an example, 3DCityDB, a widely used solution, depends on a schema having 66 tables

Externí odkaz: http://arxiv.org/abs/2307.06621

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SiNeRF: Sinusoidal Neural Radiance Fields for Joint Pose Estimation and Scene Reconstruction

Autor: Xia, Yitong, Tang, Hao, Timofte, Radu, Van Gool, Luc

NeRFmm is the Neural Radiance Fields (NeRF) that deal with Joint Optimization tasks, i.e., reconstructing real-world scenes and registering camera parameters simultaneously. Despite NeRFmm producing precise scene synthesis and pose estimations, it st

Externí odkaz: http://arxiv.org/abs/2210.04553

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Akademický článek

SCG5 and MITF may be novel markers of copper metabolism immunorelevance in Alzheimer’s disease.

Autor: Zhuang, Xianbo¹ (AUTHOR), Xia, Yitong² (AUTHOR), Liu, Yingli¹ (AUTHOR), Guo, Tingting¹ (AUTHOR), Xia, Zhangyong^1,3,4 (AUTHOR), Wang, Zheng⁵ (AUTHOR) zhengyimingdao@gmail.com, Zhang, Guifeng¹ (AUTHOR) GFZhangNo.1@gmail.com

Publikováno v: Scientific Reports. 6/13/2024, Vol. 14 Issue 1, p1-16. 16p.

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Regularizing Deep Networks with Semantic Data Augmentation

Autor: Wang, Yulin, Huang, Gao, Song, Shiji, Pan, Xuran, Xia, Yitong, Wu, Cheng

Data augmentation is widely known as a simple yet surprisingly effective technique for regularizing deep networks. Conventional data augmentation schemes, e.g., flipping, translation or rotation, are low-level, data-independent and class-agnostic ope

Externí odkaz: http://arxiv.org/abs/2007.10538

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