526 citations · 560 across the 7 of their papers we have counts for
7 papers
Resilient Sensor Fusion under Adverse Sensor Failures via Multi-Modal Expert Fusion
Konyul Park, Yecheol Kim, Daehun Kim +1
Modern autonomous driving perception systems utilize complementary multi-modal sensors, such as LiDAR and cameras. Although sensor fusion architectures enhance performance in chall…
Semi-Supervised Domain Adaptation Using Target-Oriented Domain Augmentation for 3D Object Detection
Yecheol Kim, Junho Lee, Changsoo Park +4
3D object detection is crucial for applications like autonomous driving and robotics. However, in real-world environments, variations in sensor data distribution due to sensor upgr…
Fine-Grained Pillar Feature Encoding Via Spatio-Temporal Virtual Grid for 3D Object Detection
Konyul Park, Yecheol Kim, Junho Koh +2
Developing high-performance, real-time architectures for LiDAR-based 3D object detectors is essential for the successful commercialization of autonomous vehicles. Pillar-based meth…
3D Dual-Fusion: Dual-Domain Dual-Query Camera-LiDAR Fusion for 3D Object Detection
Yecheol Kim, Konyul Park, Minwook Kim +2
Fusing data from cameras and LiDAR sensors is an essential technique to achieve robust 3D object detection. One key challenge in camera-LiDAR fusion involves mitigating the large d…
Joint 3D Object Detection and Tracking Using Spatio-Temporal Representation of Camera Image and LiDAR Point Clouds
Junho Koh, Jaekyum Kim, Jinhyuk Yoo +3
In this paper, we propose a new joint object detection and tracking (JoDT) framework for 3D object detection and tracking based on camera and LiDAR sensors. The proposed method, re…
3D-CVF: Generating Joint Camera and LiDAR Features Using Cross-View Spatial Feature Fusion for 3D Object Detection
Jin Hyeok Yoo, Yecheol Kim, Jisong Kim +1
In this paper, we propose a new deep architecture for fusing camera and LiDAR sensors for 3D object detection. Because the camera and LiDAR sensor signals have different characteri…
Robust Deep Multi-modal Learning Based on Gated Information Fusion Network
Jaekyum Kim, Junho Koh, Yecheol Kim +3
The goal of multi-modal learning is to use complimentary information on the relevant task provided by the multiple modalities to achieve reliable and robust performance. Recently,…