Zobrazeno 1 - 10
of 42
pro vyhledávání: '"Lee, Yonghan"'
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
Array Configuration-Agnostic Personalized Speech Enhancement using Long-Short-Term Spatial Coherence
Personalized speech enhancement has been a field of active research for suppression of speechlike interferers such as competing speakers or TV dialogues. Compared with single channel approaches, multichannel PSE systems can be more effective in adver
Externí odkaz:
http://arxiv.org/abs/2211.08748
Recently, speech enhancement technologies that are based on deep learning have received considerable research attention. If the spatial information in microphone signals is exploited, microphone arrays can be advantageous under some adverse acoustic
Externí odkaz:
http://arxiv.org/abs/2207.08126
Autor:
Lim, Hyungtae, Yeon, Suyong, Ryu, Soohyun, Lee, Yonghan, Kim, Youngji, Yun, Jaeseong, Jung, Euigon, Lee, Donghwan, Myung, Hyun
Global registration using 3D point clouds is a crucial technology for mobile platforms to achieve localization or manage loop-closing situations. In recent years, numerous researchers have proposed global registration methods to address a large numbe
Externí odkaz:
http://arxiv.org/abs/2203.06612
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
Teleconferencing is becoming essential during the COVID-19 pandemic. However, in real-world applications, speech quality can deteriorate due to, for example, background interference, noise, or reverberation. To solve this problem, target speech extra
Externí odkaz:
http://arxiv.org/abs/2112.05686
Autor:
Jung, Dongki, Choi, Jaehoon, Lee, Yonghan, Kim, Deokhwa, Kim, Changick, Manocha, Dinesh, Lee, Donghwan
We present a novel approach for estimating depth from a monocular camera as it moves through complex and crowded indoor environments, e.g., a department store or a metro station. Our approach predicts absolute scale depth maps over the entire scene c
Externí odkaz:
http://arxiv.org/abs/2108.05615
Autor:
Lee, Donghwan, Ryu, Soohyun, Yeon, Suyong, Lee, Yonghan, Kim, Deokhwa, Han, Cheolho, Cabon, Yohann, Weinzaepfel, Philippe, Guérin, Nicolas, Csurka, Gabriela, Humenberger, Martin
Estimating the precise location of a camera using visual localization enables interesting applications such as augmented reality or robot navigation. This is particularly useful in indoor environments where other localization technologies, such as GN
Externí odkaz:
http://arxiv.org/abs/2105.08941