26 citations · 33 across the 9 of their papers we have counts for
9 papers
Class-Distribution Guided Active Learning for 3D Occupancy Prediction in Autonomous Driving
Wonjune Kim, In-Jae Lee, Sihwan Hwang +2
3D occupancy prediction provides dense spatial understanding critical for safe autonomous driving. However, this task suffers from a severe class imbalance due to its volumetric re…
REOcc: Camera-Radar Fusion with Radar Feature Enrichment for 3D Occupancy Prediction
Chaehee Song, Sanmin Kim, Hyeonjun Jeong +3
Vision-based 3D occupancy prediction has made significant advancements, but its reliance on cameras alone struggles in challenging environments. This limitation has driven the adop…
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…
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…
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…
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…
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…
CRAFT: Camera-Radar 3D Object Detection with Spatio-Contextual Fusion Transformer
Youngseok Kim, Sanmin Kim, Jun Won Choi +1
Camera and radar sensors have significant advantages in cost, reliability, and maintenance compared to LiDAR. Existing fusion methods often fuse the outputs of single modalities at…
Diverse Multiple Trajectory Prediction Using a Two-stage Prediction Network Trained with Lane Loss
Sanmin Kim, Hyeongseok Jeon, Junwon Choi +1
Prior arts in the field of motion predictions for autonomous driving tend to focus on finding a trajectory that is close to the ground truth trajectory. Such problem formulations a…