Zobrazeno 1 - 10
of 750
pro vyhledávání: '"Choi, Jaehoon"'
Recently, 3D Gaussian splatting has gained attention for its capability to generate high-fidelity rendering results. At the same time, most applications such as games, animation, and AR/VR use mesh-based representations to represent and render 3D sce
Externí odkaz:
http://arxiv.org/abs/2410.08941
Autor:
Lee, Yonghan, Choi, Jaehoon, Jung, Dongki, Yun, Jaeseong, Ryu, Soohyun, Manocha, Dinesh, Yeon, Suyong
We present a novel-view rendering algorithm, Mode-GS, for ground-robot trajectory datasets. Our approach is based on using anchored Gaussian splats, which are designed to overcome the limitations of existing 3D Gaussian splatting algorithms. Prior ne
Externí odkaz:
http://arxiv.org/abs/2410.04646
Autor:
Maxey, Christopher, Choi, Jaehoon, Lee, Yonghan, Lee, Hyungtae, Manocha, Dinesh, Kwon, Heesung
In this paper, we present a new approach to bridge the domain gap between synthetic and real-world data for unmanned aerial vehicle (UAV)-based perception. Our formulation is designed for dynamic scenes, consisting of small moving objects or human ac
Externí odkaz:
http://arxiv.org/abs/2405.02762
Tremendous variations coupled with large degrees of freedom in UAV-based imaging conditions lead to a significant lack of data in adequately learning UAV-based perception models. Using various synthetic renderers in conjunction with perception models
Externí odkaz:
http://arxiv.org/abs/2310.16255
In this paper, an asymmetric compact multiband slot antenna is proposed for global positioning system (GPS), worldwide interoperability for microwave access (WiMAX), and wireless area network (WLAN) applications. The top plane, a ground is composed o
Externí odkaz:
http://hdl.handle.net/10150/626102
http://arizona.openrepository.com/arizona/handle/10150/626102
http://arizona.openrepository.com/arizona/handle/10150/626102
Autor:
Choi, Jaehoon, Jung, Dongki, Lee, Taejae, Kim, Sangwook, Jung, Youngdong, Manocha, Dinesh, Lee, Donghwan
We present a new pipeline for acquiring a textured mesh in the wild with a single smartphone which offers access to images, depth maps, and valid poses. Our method first introduces an RGBD-aided structure from motion, which can yield filtered depth m
Externí odkaz:
http://arxiv.org/abs/2303.15060
Monocular depth estimation in the wild inherently predicts depth up to an unknown scale. To resolve scale ambiguity issue, we present a learning algorithm that leverages monocular simultaneous localization and mapping (SLAM) with proprioceptive senso
Externí odkaz:
http://arxiv.org/abs/2203.05332
Publikováno v:
In Journal of Plastic, Reconstructive & Aesthetic Surgery November 2024 98:91-99
Autor:
Chen, Yongxiu, Lakhdar, Yazid, Chen, Lin, Kishore, Brij, Choi, Jaehoon, Williams, Ethan, Spathara, Dimitra, Jackowska, Roksana, Kendrick, Emma
Publikováno v:
In Next Energy October 2024 5
Publikováno v:
In Journal of Hazardous Materials 5 August 2024 474