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cs.CV2026

DSERT-RoLL: Robust Multi-Modal Perception for Diverse Driving Conditions with Stereo Event-RGB-Thermal Cameras, 4D Radar, and Dual-LiDAR

Hoonhee Cho, Jae-Young Kang, Yuhwan Jeong +4

In this paper, we present DSERT-RoLL, a driving dataset that incorporates stereo event, RGB, and thermal cameras together with 4D radar and dual LiDAR, collected across diverse wea…

cs.CV2025

VR-Drive: Viewpoint-Robust End-to-End Driving with Feed-Forward 3D Gaussian Splatting

Hoonhee Cho, Jae-Young Kang, Giwon Lee +4

End-to-end autonomous driving (E2E-AD) has emerged as a promising paradigm that unifies perception, prediction, and planning into a holistic, data-driven framework. However, achiev…

cs.CV2025

Unleashing the Temporal Potential of Stereo Event Cameras for Continuous-Time 3D Object Detection

Jae-Young Kang, Hoonhee Cho, Kuk-Jin Yoon

3D object detection is essential for autonomous systems, enabling precise localization and dimension estimation. While LiDAR and RGB cameras are widely used, their fixed frame rate…

cs.CV2025

From Sharp to Blur: Unsupervised Domain Adaptation for 2D Human Pose Estimation Under Extreme Motion Blur Using Event Cameras

Youngho Kim, Hoonhee Cho, Kuk-Jin Yoon

Human pose estimation is critical for applications such as rehabilitation, sports analytics, and AR/VR systems. However, rapid motion and low-light conditions often introduce motio…

cs.CV2025

Ev-3DOD: Pushing the Temporal Boundaries of 3D Object Detection with Event Cameras

Hoonhee Cho, Jae-young Kang, Youngho Kim +1

Detecting 3D objects in point clouds plays a crucial role in autonomous driving systems. Recently, advanced multi-modal methods incorporating camera information have achieved notab…

cs.CV2024

CMTA: Cross-Modal Temporal Alignment for Event-guided Video Deblurring

Taewoo Kim, Hoonhee Cho, Kuk-Jin Yoon

Video deblurring aims to enhance the quality of restored results in motion-blurred videos by effectively gathering information from adjacent video frames to compensate for the insu…