no code implementations • 2 Apr 2024 • Kyunghyun Lee, Ukcheol Shin, Byeong-Uk Lee
Adjusting camera exposure in arbitrary lighting conditions is the first step to ensure the functionality of computer vision applications.
no code implementations • 29 Mar 2024 • Byeongin Joung, Byeong-Uk Lee, Jaesung Choe, Ukcheol Shin, Minjun Kang, Taeyeop Lee, In So Kweon, Kuk-Jin Yoon
This paper proposes an algorithm for synthesizing novel views under few-shot setup.
no code implementations • CVPR 2023 • Taeyeop Lee, Jonathan Tremblay, Valts Blukis, Bowen Wen, Byeong-Uk Lee, Inkyu Shin, Stan Birchfield, In So Kweon, Kuk-Jin Yoon
Unlike previous unsupervised domain adaptation methods for category-level object pose estimation, our approach processes the test data in a sequential, online manner, and it does not require access to the source domain at runtime.
no code implementations • CVPR 2023 • Byeong-Uk Lee, Jianming Zhang, Yannick Hold-Geoffroy, In So Kweon
In this paper, we propose a single image scale estimation method based on a novel scale field representation.
1 code implementation • 12 Jan 2022 • Ukcheol Shin, Kyunghyun Lee, Byeong-Uk Lee, In So Kweon
Based on the analysis, we propose an effective thermal image mapping method that significantly increases image information, such as overall structure, contrast, and details, while preserving temporal consistency.
no code implementations • CVPR 2022 • Taeyeop Lee, Byeong-Uk Lee, Inkyu Shin, Jaesung Choe, Ukcheol Shin, In So Kweon, Kuk-Jin Yoon
Inspired by recent multi-modal UDA techniques, the proposed method exploits a teacher-student self-supervised learning scheme to train a pose estimation network without using target domain pose labels.
Ranked #5 on 6D Pose Estimation using RGBD on REAL275
no code implementations • 1 Sep 2021 • Taeyeop Lee, Byeong-Uk Lee, Myungchul Kim, In So Kweon
Our framework has two branches: the Metric Scale Object Shape branch (MSOS) and the Normalized Object Coordinate Space branch (NOCS).
no code implementations • CVPR 2021 • Byeong-Uk Lee, Kyunghyun Lee, In So Kweon
The basic framework of depth completion is to predict a pixel-wise dense depth map using very sparse input data.
1 code implementation • NeurIPS 2020 • Kyunghyun Lee, Byeong-Uk Lee, Ukcheol Shin, In So Kweon
Deep reinforcement learning (DRL) algorithms and evolution strategies (ES) have been applied to various tasks, showing excellent performances.