13 citations · 23 across the 9 of their papers we have counts for
12 papers · 1 filter
MapTCL: Temporal Consistency Learning via Bidirectional Alignment for Vectorized HD Map Construction
Hyeonseo Kim, Juyeb Shin, Hyeonjun Jeong +2
Constructing reliable online HD maps remains challenging in dynamic urban environments due to moving objects and occlusions. While recent works employ feature-level temporal fusion…
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…
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…