activity
20242026
collaborators

11 papers

cs.RO2026

LOGOS: LiDAR-Only Gaussian Elevation Splatting for Unified Tiny Obstacle Segmentation

Nan Ming, Yeqiang Qian, Chunxiang Wang +1

Robust obstacle segmentation is essential for the safety of intelligent robots, where LiDAR-based perception systems play a fundamental role in the robot-environment interaction. W…

cs.RO2026

Generating Roadside LiDAR Datasets from Vehicle-Side Datasets via Novel View Synthesis

Yuhan Xia, Runxin Zhao, Hanyang Zhuang +2

Intelligent Transportation Systems (ITS) require reliable environmental perception to support safe and efficient transportation. With the rapid development of Vehicle-to-everything…

cs.CV2026

AMFD: Distillation via Adaptive Multimodal Fusion for Multispectral Pedestrian Detection

Zizhao Chen, Yeqiang Qian, Xiaoxiao Yang +2

Multispectral pedestrian detection has been shown to be effective in improving performance within complex illumination scenarios. However, prevalent double-stream networks in multi…

cs.CV2026

RadarXFormer: Robust Object Detection via Cross-Dimension Fusion of 4D Radar Spectra and Images for Autonomous Driving

Yue Sun, Yeqiang Qian, Zhe Wang +3

Reliable perception is essential for autonomous driving systems to operate safely under diverse real-world traffic conditions. However, camera- and LiDAR-based perception systems s…

cs.CV2025

Which LiDAR scanning pattern is better for roadside perception: Repetitive or Non-repetitive?

Zhiqi Qi, Runxin Zhao, Hanyang Zhuang +2

LiDAR-based roadside perception is a cornerstone of advanced Intelligent Transportation Systems (ITS). While considerable research has addressed optimal LiDAR placement for infrast…

cs.RO2025

Bench-RNR: Dataset for Benchmarking Repetitive and Non-repetitive Scanning LiDAR for Infrastructure-based Vehicle Localization

Runxin Zhao, Chunxiang Wang, Hanyang Zhuang +1

Vehicle localization using roadside LiDARs can provide centimeter-level accuracy for cloud-controlled vehicles while simultaneously serving multiple vehicles, enhanc-ing safety and…