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Euntai Kim

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12 papers

2 author rows

AAAI Conference 2026 Conference Paper

A²LC: Active and Automated Label Correction for Semantic Segmentation

Youjin Jeon
Kyusik Cho
Suhan Woo
Euntai Kim

Active Label Correction (ALC) has emerged as a promising solution to the high cost and error-prone nature of manual pixel-wise annotation in semantic segmentation, by actively identifying and correcting mislabeled data. Although recent work has improved correction efficiency by generating pseudo-labels using foundation models, substantial inefficiencies still remain. In this paper, we introduce A²LC, an Active and Automated Label Correction framework for semantic segmentation, where manual and automatic correction stages operate in a cascaded manner. Specifically, the automatic correction stage leverages human feedback to extend label corrections beyond the queried samples, thereby maximizing cost efficiency. In addition, we introduce an adaptively balanced acquisition function that emphasizes underrepresented tail classes, working in strong synergy with the automatic correction stage. Extensive experiments on Cityscapes and PASCAL VOC 2012 demonstrate that A²LC significantly outperforms previous state-of-the-art methods. Notably, A²LC exhibits high efficiency by outperforming previous methods with only 20% of their budget, and shows strong effectiveness by achieving a 27.23% performance gain under the same budget on Cityscapes.

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ICRA Conference 2025 Conference Paper

Fast Global Localization on Neural Radiance Field

Mangyu Kong
Jaewon Lee
Seongwon Lee 0002
Euntai Kim

Neural Radiance Fields (NeRF) presented a novel way to represent scenes, allowing for high-quality 3D reconstruction from 2D images. Following its remarkable achievements, global localization within NeRF maps is an essential task for enabling a wide range of applications. Recently, Loc-NeRF demonstrated a localization approach that combines traditional Monte Carlo Localization with NeRF, showing promising results for using NeRF as an environment map. However, despite its advancements, Loc-NeRF encounters the challenge of a time-intensive ray rendering process, which can be a significant limitation in practical applications. To address this issue, we introduce Fast Loc-NeRF, which enhances efficiency and accuracy in NeRF map-based global localization. We propose a particle rejection weighting strategy that estimates the uncertainty of particles by leveraging NeRF's inherent characteristics and incorporates them into the particle weighting process to reject abnormal particles. Additionally, Fast Loc-NeRF employs a coarse-to-fine approach, matching rendered pixels and observed images across multiple resolutions from low to high. As a result, it speeds up the costly particle update process while enhancing precise localization results. Our Fast Loc-NeRF establishes new state-of-the-art localization performance on several bench-marks, demonstrating both its accuracy and efficiency. The code is available at this url.

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ICRA Conference 2025 Conference Paper

RE-TRIP: Reflectivity Instance Augmented Triangle Descriptor for 3D Place Recognition

Yechan Park
Gyuhyeon Pak
Euntai Kim

While most people associate LiDAR primarily with its ability to measure distances and provide geometric information about the environment (via point clouds), LiDAR also captures additional data, including reflectivity or intensity values. Unfortunately, when LiDAR is applied to Place Recognition (PR) in mobile robotics, most previous works on LiDAR-based PR rely only on geometric measurements, neglecting the additional reflectivity information that LiDAR provides. In this paper, we propose a novel descriptor for 3D PR, named RE-TRIP (REflectivity-instance augmented TRIangle descriPtor). This new descriptor leverages both geometric measurements and reflectivity to enhance robustness in challenging scenarios such as geometric degeneracy, high geometric similarity, and the presence of dynamic objects. To implement RE-TRIP in real-world applications, we further propose (1) keypoint extraction method, (2) key instance segmentation method, (3) RE-TRIP matching method, and (4) reflectivity combined loop verification method. Finally, we conduct a series of experiments to demonstrate the effectiveness of RE-TRIP. Applied to public datasets (i. e. , HELIPR, FusionPortable) containing diverse scenarios-including long corridors, bridges, large-scale urban areas, and highly dynamic environments-our experimental results show that the proposed method outperforms existing state-of-the-art methods in terms of Scan Context, Intensity Scan Context and STD. Our code is available at: https://github.com/pycS714IRE-TRIP.

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AAAI Conference 2025 Conference Paper

Zero-Shot Scene Change Detection

Kyusik Cho
Dong Yeop Kim
Euntai Kim

We present a novel, training-free approach to scene change detection. Our method leverages tracking models, which inherently perform change detection between consecutive frames of video by identifying common objects and detecting new or missing objects. Specifically, our method takes advantage of the change detection effect of the tracking model by inputting reference and query images instead of consecutive frames. Furthermore, we focus on the content gap and style gap between two input images in change detection, and address both issues by proposing adaptive content threshold and style bridging layers, respectively. Finally, we extend our approach to video, leveraging rich temporal information to enhance the performance of scene change detection. We compare our approach and baseline through various experiments. While existing train-based baseline tend to specialize only in the trained domain, our method shows consistent performance across various domains, proving the competitiveness of our approach.

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AAAI Conference 2023 Conference Paper

SHUNIT: Style Harmonization for Unpaired Image-to-Image Translation

Seokbeom Song
Suhyeon Lee
Hongje Seong
Kyoungwon Min
Euntai Kim

We propose a novel solution for unpaired image-to-image (I2I) translation. To translate complex images with a wide range of objects to a different domain, recent approaches often use the object annotations to perform per-class source-to-target style mapping. However, there remains a point for us to exploit in the I2I. An object in each class consists of multiple components, and all the sub-object components have different characteristics. For example, a car in CAR class consists of a car body, tires, windows and head and tail lamps, etc., and they should be handled separately for realistic I2I translation. The simplest solution to the problem will be to use more detailed annotations with sub-object component annotations than the simple object annotations, but it is not possible. The key idea of this paper is to bypass the sub-object component annotations by leveraging the original style of the input image because the original style will include the information about the characteristics of the sub-object components. Specifically, for each pixel, we use not only the per-class style gap between the source and target domains but also the pixel’s original style to determine the target style of a pixel. To this end, we present Style Harmonization for unpaired I2I translation (SHUNIT). Our SHUNIT generates a new style by harmonizing the target domain style retrieved from a class memory and an original source image style. Instead of direct source-to-target style mapping, we aim for source and target styles harmonization. We validate our method with extensive experiments and achieve state-of-the-art performance on the latest benchmark sets. The source code is available online: https://github.com/bluejangbaljang/SHUNIT.

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AAAI Conference 2022 Conference Paper

Graph-Based Point Tracker for 3D Object Tracking in Point Clouds

Minseong Park
Hongje Seong
Wonje Jang
Euntai Kim

In this paper, a new deep learning network named as graphbased point tracker (GPT) is proposed for 3D object tracking in point clouds. GPT is not based on Siamese network applied to template and search area, but it is based on the transfer of target clue from the template to the search area. GPT is endto-end trainable. GPT has two new modules: graph feature augmentation (GFA) and improved target clue (ITC) module. The key idea of GFA is to exploit one-to-many relationship between template and search area points using a bipartite graph. In GFA, edge features of the bipartite graph are generated by transferring the target clues of template points to search area points through edge convolution. It captures the relationship between template and search area points effectively from the perspective of geometry and shape of two point clouds. The second module is ITC. The key idea of ITC is to embed the information of the center of the target into the edges of the bipartite graph via Hough voting, strengthening the discriminative power of GFA. Both modules significantly contribute to the improvement of GPT by transferring geometric and shape information including target center from target template to search area effectively. Experiments on the KITTI tracking dataset show that GPT achieves state-of-theart performance and can run in real-time.

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AAAI Conference 2022 Conference Paper

Iteratively Selecting an Easy Reference Frame Makes Unsupervised Video Object Segmentation Easier

Youngjo Lee
Hongje Seong
Euntai Kim

Unsupervised video object segmentation (UVOS) is a perpixel binary labeling problem which aims at separating the foreground object from the background in the video without using the ground truth (GT) mask of the foreground object. Most of the previous UVOS models use the first frame or the entire video as a reference frame to specify the mask of the foreground object. Our question is why the first frame should be selected as a reference frame or why the entire video should be used to specify the mask. We believe that we can select a better reference frame to achieve the better UVOS performance than using only the first frame or the entire video as a reference frame. In our paper, we propose Easy Frame Selector (EFS). The EFS enables us to select an “easy” reference frame that makes the subsequent VOS become easy, thereby improving the VOS performance. Furthermore, we propose a new framework named as Iterative Mask Propagation (IMP). In the framework, we repeat applying EFS to the given video and selecting an “easier” reference frame from the video than the previous iteration, increasing the VOS performance incrementally. The IMP consists of EFS, Bi-directional Mask Prediction (BMP), and Temporal Information Updating (TIU). From the proposed framework, we achieve state-of-the-art performance in three UVOS benchmark sets: DAVIS16, FBMS, and SegTrack-V2.

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AAAI Conference 2021 Conference Paper

Unsupervised Domain Adaptation for Semantic Segmentation by Content Transfer

Suhyeon Lee
Junhyuk Hyun
Hongje Seong
Euntai Kim

In this paper, we tackle the unsupervised domain adaptation (UDA) for semantic segmentation, which aims to segment the unlabeled real data using labeled synthetic data. The main problem of UDA for semantic segmentation relies on reducing the domain gap between the real image and synthetic image. To solve this problem, we focused on separating information in an image into content and style. Here, only the content has cues for semantic segmentation, and the style makes the domain gap. Thus, precise separation of content and style in an image leads to effect as supervision of real data even when learning with synthetic data. To make the best of this effect, we propose a zero-style loss. Even though we perfectly extract content for semantic segmentation in the real domain, another main challenge, the class imbalance problem, still exists in UDA for semantic segmentation. We address this problem by transferring the contents of tail classes from synthetic to real domain. Experimental results show that the proposed method achieves the state-of-the-art performance in semantic segmentation on the major two UDA settings.

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IROS Conference 2019 Conference Paper

Normal Distribution Mixture Matching based Model Free Object Tracking Using 2D LIDAR

Baehoon Choi
HyungGi Jo
Euntai Kim

In this paper, a novel normal distribution mixture matching based model free object tracking algorithm using 2D LIDAR is proposed. Each target object is modeled as a normal distribution mixture that captures the distribution of the points scanned from the surface of the object. This novel representation enables normal distribution transform (NDT) to accurately estimate the motion of objects, even if the shape of the points differs depending on where it is observed. Our evaluation of the proposed algorithm shows good performance in practical applications. In addition, we provides an alternative way of segmentation and data association using occupancy grid map to avoid a problem that defines a distance metric between the mixture and the point cloud. As a result, the proposed algorithm works in real time in our experiments.

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IROS Conference 2006 Conference Paper

Flying Display: Autonomous Blimp with Real-Time Visual Tracking and Image Projection

Seungyong Oh
Sungchul Kang
Kyung Joon Lee
Sang Chul Ahn
Euntai Kim

This paper presents a flying display system using an autonomous blimp (small indoor airship) with a visual tracking system and an image projection system. The real-time visual tracking system tracks the blimp while it flies along a given spatial path to follow a wall. The image projection system projects still images or a video stream, whose rectangular shape is pre-compensated to look natural and flat using an image warping algorithm, on the surface of the blimp. The blimp is designed to have holonomic dynamics and it can maintain a stable pose and position in the presence of bounded air flow disturbances during the wall following motion. The real-time visual tracking system tracks the blimp and calculates its position in three dimensional space. Finally, we verify the capability of the autonomous blimp, the real-time visual tracking and the image projection system by experiments in the public exhibition environment. We also verify that the system is useful for transferring information and advertising in a crowded public area such as an exhibition hall or a department store

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IROS Conference 1998 Conference Paper

Variable structure control of manipulator using linear time-varying sliding surfaces

Heejin Lee
Hyunseok Shin
Euntai Kim
Seungwoo Kim
Mignon Park

This paper proposes a new variable structure controller (VSC) for the accurate tracking control of a manipulator using the variable boundary layer. Up to now, VSC applying the variable boundary layer did not remove chattering from an arbitrary initial state of the system trajectory because VSC has used the fixed sliding surface. But, by using the linear time-varying sliding surfaces, the scheme has the robustness against chattering from all states. This scheme can be applied to the second-order nonlinear systems with parameter uncertainty and extraneous disturbances, and have better tracking performance than the conventional method. To demonstrate its performance, the proposed control algorithm is applied to a two-link manipulator.

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IROS Conference 1993 Conference Paper

A new fuzzy adaptive controller using a robust property of fuzzy controller

S. W. Kim
Euntai Kim
Mignon Park

A fuzzy adaptive controller which is able to solve the problems of classical adaptive controllers and conventional fuzzy adaptive controller is suggested. The architecture of a fuzzy adaptive controller using the robust property of a fuzzy controller is explained. A design procedure which can be carried out mathematically and systematically from the model of an objective system is suggested, and related mathematical theorems and their proofs are also given. The performance of the proposed adaptive control algorithm is analyzed through a DC motor control simulation and experiments on a compliant robot system.

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