1 citations · 1 across the 5 of their papers we have counts for
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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…
RC-GeoCP: Geometric Consensus for 4D Radar-Camera Collaborative Perception
Xiaokai Bai, Lianqing Zheng, Runwei Guan +3
Collaborative perception (CP) extends sensing range through feature sharing, but most systems remain LiDAR-centric. Camera and 4D radar sensing combines dense semantics with lower-…
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…
Event-aided Semantic Scene Completion
Shangwei Guo, Hao Shi, Song Wang +3
Autonomous driving systems rely on robust 3D scene understanding. Recent advances in Semantic Scene Completion (SSC) for autonomous driving underscore the limitations of RGB-based…
GenMapping: Unleashing the Potential of Inverse Perspective Mapping for Robust Online HD Map Construction
Siyu Li, Kailun Yang, Hao Shi +3
Online High-Definition (HD) maps have emerged as the preferred option for autonomous driving, overshadowing the counterpart offline HD maps due to flexible update capability and lo…
RegTrack: Simplicity Beneath Complexity in Robust Multi-Modal 3D Multi-Object Tracking
Lipeng Gu, Xuefeng Yan, Song Wang +1
Existing 3D multi-object tracking (MOT) methods often sacrifice efficiency and generalizability for robustness, largely relying on complex association metrics derived from multi-mo…