10 papers · 1 filter
Horizon3D: Sparse Radar-Camera Fusion for Long-Range 3D Perception in Autonomous Driving
Geonho Bang, Geunju Baek, Dongyoung Lee +2
Long-range 3D object detection is critical for safe autonomous driving at highway speeds, yet existing radar-camera fusion methods remain limited at extended ranges. BEV-based meth…
Mining Instance-Centric Vision-Language Contexts for Human-Object Interaction Detection
Soo Won Seo, KyungChae Lee, Hyungchan Cho +3
Human-Object Interaction (HOI) detection aims to localize human-object pairs and classify their interactions from a single image, a task that demands strong visual understanding an…
SafeDrive: Fine-Grained Safety Reasoning for End-to-End Driving in a Sparse World
Jungho Kim, Jiyong Oh, Seunghoon Yu +3
The end-to-end (E2E) paradigm, which maps sensor inputs directly to driving decisions, has recently attracted significant attention due to its unified modeling capability and scala…
RCTDistill: Cross-Modal Knowledge Distillation Framework for Radar-Camera 3D Object Detection with Temporal Fusion
Geonho Bang, Minjae Seong, Jisong Kim +5
Radar-camera fusion methods have emerged as a cost-effective approach for 3D object detection but still lag behind LiDAR-based methods in performance. Recent works have focused on…
MAESTRO: Task-Relevant Optimization via Adaptive Feature Enhancement and Suppression for Multi-task 3D Perception
Changwon Kang, Jisong Kim, Hongjae Shin +2
The goal of multi-task learning is to learn to conduct multiple tasks simultaneously based on a shared data representation. While this approach can improve learning efficiency, it…
RadarDistill: Boosting Radar-based Object Detection Performance via Knowledge Distillation from LiDAR Features
Geonho Bang, Kwangjin Choi, Jisong Kim +2
The inherent noisy and sparse characteristics of radar data pose challenges in finding effective representations for 3D object detection. In this paper, we propose RadarDistill, a…