Ukcheol Shin

Carnegie Mellon University

Author Statistics

Papers

Citation

H-Index

i-10 index

Research Trends

Author Order

Document Type

Co-Authors

In So Kweon

Kootenay Association for Science & Technology

Kyunghyun Lee

Korea Advanced Institute of Science and Technology

Byeong-Uk Lee

Kraft Heinz (United States)

Jean Oh

Carnegie Mellon University

Jinsun Park

Pusan National University

Taeyeop Lee

Korea Advanced Institute of Science and Technology

Jaesung Choe

Korea Advanced Institute of Science and Technology

François Rameau

SUNY Korea

Sunghoon Im

Daegu Gyeongbuk Institute of Science and Technology

Gyumin Shim

Korea Advanced Institute of Science and Technology

Cooperative Institutions

Korea Advanced Institute of Science and Technology

Kootenay Association for Science & Technology

Carnegie Mellon University

Kyung Hee University

Seoul National University

Ulsan National Institute of Science and Technology

Daejeon Institute of Science and Technology

Hanyang University

Pusan National University

Seoul National University of Science and Technology

Author Statistics

Papers

Citation

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i-10 index

Research Field

Self-Supervised Depth and Ego-Motion Estimation for Monocular Thermal Video Using Multi-Spectral Consistency Loss

IEEE Robotics and Automation Letters (2021)

Ukcheol Shin Kyunghyun Lee Seokju Lee In So Kweon

A thermal camera can robustly capture thermal radiation images under harsh light conditions such as night scenes, tunnels, and disaster scenarios. However, despite this advantage, neither depth nor ego-motion estimation research for the thermal camera have not been actively explored so far. In this paper, we propose a self-supervised learning method for depth and ego-motion estimation from thermal images. The proposed method exploits multi-spectral consistency that consists of temperature and photometric consistency loss. The temperature consistency loss provides a fundamental self-supervisory signal by reconstructing clipped and colorized thermal images. Additionally, we design a differentiable forward warping module that can transform the coordinate system of the estimated depth map and relative pose from thermal camera to visible camera. Based on the proposed module, the photometric consistency loss can provide complementary self-supervision to networks. Networks trained with the proposed method robustly estimate the depth and pose from monocular thermal video under low-light and even zero-light conditions. To the best of our knowledge, this is the first work to simultaneously estimate both depth and ego-motion from monocular thermal video in a self-supervised manner.

Monocular

Image warping

10.1109/lra.2021.3137895

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Citations (11)

Flow4D: Leveraging 4D Voxel Network for LiDAR Scene Flow Estimation

IEEE Robotics and Automation Letters (2025)

Jae-Yeul Kim Jungwan Woo Ukcheol Shin Jean Oh Sunghoon Im

10.1109/lra.2025.3542327

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Citations (0)

Joint self-supervised learning and adversarial adaptation for monocular depth estimation from thermal image

Machine Vision and Applications (2023)

Ukcheol Shin Kwanyong Park Kyunghyun Lee Byeong-Uk Lee In So Kweon

Leverage (statistics)

Monocular

Robustness

Ground truth

Feature (linguistics)

10.1007/s00138-023-01404-3

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Citations (2)

Stable Surface Regularization for Fast Few-Shot NeRF

2021 International Conference on 3D Vision (3DV) (2024)

Byeongin Joung Byeong-Uk Lee Jaesung Choe Ukcheol Shin Minjun Kang

This paper proposes an algorithm for synthesizing novel views under few-shot setup. The main concept is to develop a stable surface regularization technique called Annealing Signed Distance Function (ASDF), which anneals the surface in a coarse-to-fine manner to accelerate convergence speed. We observe that the Eikonal loss – which is a widely known geometric regularization – requires dense training signal to shape different level-sets of SDF, leading to low-fidelity results under few-shot training. In contrast, the proposed surface regularization successfully reconstructs scenes and produce high-fidelity geometry with stable training. Our method is further accelerated by utilizing grid representation and monocular geometric priors. Finally, the proposed approach is up to 45 times faster than existing few-shot novel view synthesis methods, and it produces comparable results in the ScanNet dataset and NeRF-Real dataset.

Regularization

10.1109/3dv62453.2024.00147

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Citations (0)

Complementary Random Masking for RGB-Thermal Semantic Segmentation

arXiv (Cornell University) (2023)

Ukcheol Shin Kyunghyun Lee In So Kweon

RGB-thermal semantic segmentation is one potential solution to achieve reliable semantic scene understanding in adverse weather and lighting conditions. However, the previous studies mostly focus on designing a multi-modal fusion module without consideration of the nature of multi-modality inputs. Therefore, the networks easily become over-reliant on a single modality, making it difficult to learn complementary and meaningful representations for each modality. This paper proposes 1) a complementary random masking strategy of RGB-T images and 2) self-distillation loss between clean and masked input modalities. The proposed masking strategy prevents over-reliance on a single modality. It also improves the accuracy and robustness of the neural network by forcing the network to segment and classify objects even when one modality is partially available. Also, the proposed self-distillation loss encourages the network to extract complementary and meaningful representations from a single modality or complementary masked modalities. Based on the proposed method, we achieve state-of-the-art performance over three RGB-T semantic segmentation benchmarks. Our source code is available at https://github.com/UkcheolShin/CRM_RGBTSeg.

Modality (human–computer interaction)

RGB color model

Modalities

Robustness

10.48550/arxiv.2303.17386

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Citations (1)

Learning Quadrupedal Locomotion with Impaired Joints Using Random Joint Masking

Min‐Cheol Kim Ukcheol Shin Jung-Yup Kim

Quadrupedalism

10.1109/icra57147.2024.10610088

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Citations (2)

Vehicular Multi-Camera Sensor System for Automated Visual Inspection of Electric Power Distribution Equipment

2021 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) (2019)

Jinsun Park Ukcheol Shin Gyumin Shim Kyungdon Joo François Rameau

In this paper, we present a multi-camera sensor system along with its control algorithm for automated visual inspection from a moving vehicle. To accomplish this task, we propose a unique hardware configuration consisting of a frontal stereo vision system, six lateral cameras motorized to tilt, and a GPS/IMU sensor mounted on the roof of a car. From the frontal stereo system, we detect electric poles and estimate their corresponding 3D positions. Based on this 3D estimation, the tilt angles of the motorized lateral cameras are controlled in real-time to capture high resolution images of the equipment - typically installed a few meters above the road surface. In addition, inertial odometry information from the GPS/IMU module is utilized for pose estimation, object localization, and re-identification among cameras. Experimental results demonstrate the efficiency and robustness of our system for automated electric equipment maintenance, which can reduce human effort significantly.

Visual Odometry

Robustness

Stereo cameras

10.1109/iros40897.2019.8968085

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Citations (6)

Unpaired Shadow Removal: Enhancing Attention to Shadow Areas via Dropkey

2020 International Conference on Electronics, Information, and Communication (ICEIC) (2025)

Jaewon Yang Ukcheol Shin Donghyeon Cho

10.1109/iceic64972.2025.10879608

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Citations (0)

UDA-COPE: Unsupervised Domain Adaptation for Category-level Object Pose Estimation

2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) (2022)

Taeyeop Lee Byeong-Uk Lee Inkyu Shin Jaesung Choe Ukcheol Shin

Learning to estimate object pose often requires ground-truth (GT) labels, such as CAD model and absolute-scale object pose, which is expensive and laborious to obtain in the real world. To tackle this problem, we propose an unsupervised domain adaptation (UDA) for category-level object pose estimation, called UDA-COPE. 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. We also introduce a bidirectional filtering method between the predicted normalized object coordinate space (NOCS) map and observed point cloud, to not only make our teacher network more robust to the target domain but also to provide more reliable pseudo labels for the student network training. Extensive experimental results demonstrate the effectiveness of our proposed method both quantitatively and qualitatively. Notably, without leveraging target-domain GT labels, our proposed method achieved comparable or sometimes superior performance to existing methods that depend on the GT labels.

Ground truth

Domain Adaptation

10.1109/cvpr52688.2022.01447

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Citations (29)

Stable Surface Regularization for Fast Few-Shot NeRF

arXiv (Cornell University) (2024)

Byeongin Joung Byeong-Uk Lee Jaesung Choe Ukcheol Shin Minjun Kang

This paper proposes an algorithm for synthesizing novel views under few-shot setup. The main concept is to develop a stable surface regularization technique called Annealing Signed Distance Function (ASDF), which anneals the surface in a coarse-to-fine manner to accelerate convergence speed. We observe that the Eikonal loss - which is a widely known geometric regularization - requires dense training signal to shape different level-sets of SDF, leading to low-fidelity results under few-shot training. In contrast, the proposed surface regularization successfully reconstructs scenes and produce high-fidelity geometry with stable training. Our method is further accelerated by utilizing grid representation and monocular geometric priors. Finally, the proposed approach is up to 45 times faster than existing few-shot novel view synthesis methods, and it produces comparable results in the ScanNet dataset and NeRF-Real dataset.

Regularization

Single shot

10.48550/arxiv.2403.19985

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Citations (0)