Fully Geometric Panoramic Localization
Junho Kim, Jiwon Jeong, Young Min Kim
CVPR 2024
[arXiv] [Code] [Paper] [Video] [Project Page]

By leveraging lines and their intersections, we can perform panoramic localization without using any visual features, in a fully geometric manner.

LDL: Line Distance Functions for Panoramic Localization
Junho Kim, Changwoon Choi, Hojun Jang, Young Min Kim
ICCV 2023
[arXiv] [Code] [Paper] [Video] [Project Page]

We introduce a simple pose search method that operates on 2D, 3D line segments based on distance functions, which attains competitive performance with a very short runtime.

Calibrating  Panoramic Depth Estimation for Practical Localization and Mapping
Junho Kim, Eunsun Lee, Young Min Kim
ICCV 2023
[arXiv] [Code] [Paper] [Video] [Project Page]

With a simple test-time adaptation scheme, we can 'calibrate' panoramic depth estimation algorithms to make more robust predictions and further improve on downstream tasks in localization and mapping.

Privacy-Preserving Visual  Localization with Event Cameras
Junho Kim, Young Min Kim,  Yicheng Wu, Ramzi Zahreddine, Weston Anthony Welge, Gurunandan Krishnan, Sizhuo Ma, Jian Wang 
arXiv  preprint, 2022.
[arXiv] [Code] [Paper] [Video] [Project Page]

We propose a event-based visual localization method that effectively leverages the strengths of event cameras while offering privacy protection for alleviating user concerns in client-server localization scenarios. 

Ev-NeRF:  Event Based Neural Radiance Field
Inwoo Hwang, Junho Kim, and Young Min Kim
WACV 2023
[arXiv] [Paper] [Video] [Project Page]

Neural radiance fields (NeRF) for event cameras can be learned without using any intensity image supervision, and enables robust high-dynamic range imaging amidst large amounts of sensor noise.

CPO: Change Robust Panorama to Point  Cloud Localization
Junho Kim, Hojun Jang, Changwoon Choi, and Young Min Kim
ECCV 2022
[arXiv] [Code] [Paper] [Video] [Project Page]

Constructing score maps in 2D, 3D that reflect regional color consistencies enable robust localization amidst scene changes.

MoDA:  Map style transfer for self-supervised Domain Adaptation of embodied agents
Eunsun Lee, Junho Kim, Sangwon Park, and Young Min Kim
ECCV 2022
[arXiv] [Paper] [Video] [Project Page]

By applying style transfer on the grid map domain, effective domain adaptation is possible for environments containing visual or dynamic noise.

Ev-TTA: Test-Time Adaptation for Event-Based Object Recognition
Junho Kim, Inwoo Hwang, and Young Min Kim
CVPR 2022
[arXiv] [Code] [Paper] [Video] [Project Page]

A simple test-time adaptation objective can help event-based classifiers to make robust predictions in extreme conditions such as large motion or low lighting. 

Self-Supervised Domain Adaptation for Visual Navigation with Global Map Consistency
Eunsun Lee, Junho Kim, and Young Min Kim
WACV 2022 
[arXiv] [Paper] [Video] [Project Page]

Imposing global map consistency by performing round-trips can enhance the navigation performance of embodied agents amidst various dynamics corruptions.

SGoLAM: Simultaneous Goal Localization and Mapping for Multi-Object Goal Navigation
Junho Kim, Eunsun Lee, Mingi Lee, Dongsu Zhang, and Young Min Kim
arXiv preprint, 2021. (2nd place in the MultiON challenge @ Embodied AI workshop in CVPR 2021) 
[arXiv] [Code] [Video]

By performing goal localization and mapping simultaneously with a simple color matching scheme, embodied agents can effectively perform multi-object goal navigation.

PICCOLO: Point Cloud-Centric Omnidirectional Localization
Junho Kim, Changwoon Choi, Hojun Jang, and Young Min Kim
ICCV 2021 
[arXiv] [Code] [Paper] [Video] [Project Page]

By minimizing a novel loss function that penalizes color discrepancies in 2D and 3D, effective localization can be performed using panorama images without learning.

N-ImageNet: Towards Robust, Fine-Grained Object Recognition with Event Cameras
Junho Kim, Jaehyeok Bae, Gangin Park, Dongsu Zhang, and Young Min Kim
ICCV 2021 
[arXiv] [Code] [Paper] [Video] [Project Page]

We propose a large scale dataset for event-based object recognition that enables systematic quantification of classification robustness amidst extreme lighting or motion.