most citedBoosting Monocular 3D Object Detection with Object-Centric Auxiliary Depth Supervision

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cs.CV2025

CRAB: Camera-Radar Fusion for Reducing Depth Ambiguity in Backward Projection based View Transformation

In-Jae Lee, Sihwan Hwang, Youngseok Kim +3

Recently, camera-radar fusion-based 3D object detection methods in bird's eye view (BEV) have gained attention due to the complementary characteristics and cost-effectiveness of th…

cs.CV2024

LabelDistill: Label-guided Cross-modal Knowledge Distillation for Camera-based 3D Object Detection

Sanmin Kim, Youngseok Kim, Sihwan Hwang +2

Recent advancements in camera-based 3D object detection have introduced cross-modal knowledge distillation to bridge the performance gap with LiDAR 3D detectors, leveraging the pre…

cs.CV2023

Predict to Detect: Prediction-guided 3D Object Detection using Sequential Images

Sanmin Kim, Youngseok Kim, In-Jae Lee +1

Recent camera-based 3D object detection methods have introduced sequential frames to improve the detection performance hoping that multiple frames would mitigate the large depth es…

cs.CV2023

CRN: Camera Radar Net for Accurate, Robust, Efficient 3D Perception

Youngseok Kim, Juyeb Shin, Sanmin Kim +3

Autonomous driving requires an accurate and fast 3D perception system that includes 3D object detection, tracking, and segmentation. Although recent low-cost camera-based approache…

cs.CV20221 cited

Boosting Monocular 3D Object Detection with Object-Centric Auxiliary Depth Supervision

Youngseok Kim, Sanmin Kim, Sangmin Sim +2

Recent advances in monocular 3D detection leverage a depth estimation network explicitly as an intermediate stage of the 3D detection network. Depth map approaches yield more accur…