9 papers · 1 filter
InfraWorld: Motion-Aware World Modeling for Roadside 4D Occupancy Forecasting
Songkai Wang, Xiaokai Bai, Lei Yang +1
Roadside 4D occupancy forecasting predicts future semantic occupancy in a fixed infrastructure coordinate system. Roadside scenes are globally structured yet locally evolving, requ…
4DR360: State Reasoning for Joint 3D Detection and Occupancy Prediction in 4D Radar-Camera Full-Scene Perception
Xiaokai Bai, Lianqing Zheng, Runwei Guan +3
Reliable autonomous driving requires full-scene perception that couples foreground objects with dense semantic layout. Recently, 4D millimeter-wave radar has emerged as a robust an…
SD4R: Sparse-to-Dense Learning for 3D Object Detection with 4D Radar
Xiaokai Bai, Jiahao Cheng, Songkai Wang +5
4D radar measurements offer an affordable and weather-robust solution for 3D perception. However, the inherent sparsity and noise of radar point clouds present significant challeng…
Boosting Instance Awareness via Cross-View Correlation with 4D Radar and Camera for 3D Object Detection
Xiaokai Bai, Lianqing Zheng, Si-Yuan Cao +6
4D millimeter-wave radar has emerged as a promising sensing modality for autonomous driving due to its robustness and affordability. However, its sparse and weak geometric cues mak…
S-BEVLoc: BEV-based Self-supervised Framework for Large-scale LiDAR Global Localization
Chenghao Zhang, Lun Luo, Si-Yuan Cao +6
LiDAR-based global localization is an essential component of simultaneous localization and mapping (SLAM), which helps loop closure and re-localization. Current approaches rely on…
Boosting Multi-View Indoor 3D Object Detection via Adaptive 3D Volume Construction
Runmin Zhang, Zhu Yu, Si-Yuan Cao +4
This work presents SGCDet, a novel multi-view indoor 3D object detection framework based on adaptive 3D volume construction. Unlike previous approaches that restrict the receptive…
RaGS: Unleashing 3D Gaussian Splatting from 4D Radar and Monocular Cues for 3D Object Detection
Xiaokai Bai, Chenxu Zhou, Lianqing Zheng +6
4D millimeter-wave radar is a promising sensing modality for autonomous driving, yet effective 3D object detection from 4D radar and monocular images remains challenging. Existing…
CDFormer: Cross-Domain Few-Shot Object Detection Transformer Against Feature Confusion
Boyuan Meng, Xiaohan Zhang, Peilin Li +5
Cross-domain few-shot object detection (CD-FSOD) aims to detect novel objects across different domains with limited class instances. Feature confusion, including object-background…
Doracamom: Joint 3D Detection and Occupancy Prediction with Multi-view 4D Radars and Cameras for Omnidirectional Perception
Lianqing Zheng, Jianan Liu, Runwei Guan +8
3D object detection and occupancy prediction are critical tasks in autonomous driving, attracting significant attention. Despite the potential of recent vision-based methods, they…