no code implementations • 16 Nov 2022 • Yicheng Hsu, Yonghan Lee, Mingsian R. Bai
Personalized speech enhancement has been a field of active research for suppression of speechlike interferers such as competing speakers or TV dialogues.
no code implementations • 17 Jul 2022 • Yicheng Hsu, Yonghan Lee, Mingsian R. Bai
Recently, speech enhancement technologies that are based on deep learning have received considerable research attention.
no code implementations • 13 Mar 2022 • Hyungtae Lim, Suyong Yeon, Soohyun Ryu, Yonghan Lee, Youngji Kim, JaeSeong Yun, Euigon Jung, Donghwan Lee, Hyun Myung
As verified in indoor and outdoor 3D LiDAR datasets, our proposed method yields robust global registration performance compared with other global registration methods, even for distant point cloud pairs.
no code implementations • 10 Mar 2022 • Jaehoon Choi, Dongki Jung, Yonghan Lee, Deokhwa Kim, Dinesh Manocha, Donghwan Lee
Given these metric poses and monocular sequences, we propose a self-supervised learning method for the pre-trained supervised monocular depth networks to enable metrically scaled depth estimation.
no code implementations • 10 Dec 2021 • Yicheng Hsu, Yonghan Lee, Mingsian R. Bai
Furthermore, the proposed enhancement system was compared with a baseline system with speaker embeddings and interchannel phase difference.
no code implementations • ICCV 2021 • Dongki Jung, Jaehoon Choi, Yonghan Lee, Deokhwa Kim, Changick Kim, Dinesh Manocha, Donghwan Lee
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.
no code implementations • CVPR 2021 • Donghwan Lee, Soohyun Ryu, Suyong Yeon, Yonghan Lee, Deokhwa Kim, Cheolho Han, Yohann Cabon, Philippe Weinzaepfel, Nicolas Guérin, Gabriela Csurka, Martin Humenberger
In this paper, we introduce 5 new indoor datasets for visual localization in challenging real-world environments.
no code implementations • 10 Nov 2020 • Jaehoon Choi, Dongki Jung, Yonghan Lee, Deokhwa Kim, Dinesh Manocha, Donghwan Lee
We present a novel algorithm for self-supervised monocular depth completion.