3 papers
cs.CV2026
DLWM: Dual Latent World Models enable Holistic Gaussian-centric Pre-training in Autonomous Driving
Yiyao Zhu, Ying Xue, Haiming Zhang +8
Vision-based autonomous driving has gained much attention due to its low costs and excellent performance. Compared with dense BEV (Bird's Eye View) or sparse query models, Gaussian…
cs.CV2025
SQS: Enhancing Sparse Perception Models via Query-based Splatting in Autonomous Driving
Haiming Zhang, Yiyao Zhu, Wending Zhou +5
Sparse Perception Models (SPMs) adopt a query-driven paradigm that forgoes explicit dense BEV or volumetric construction, enabling highly efficient computation and accelerated infe…
cs.CV2025
VisionPAD: A Vision-Centric Pre-training Paradigm for Autonomous Driving
Haiming Zhang, Wending Zhou, Yiyao Zhu +7
This paper introduces VisionPAD, a novel self-supervised pre-training paradigm designed for vision-centric algorithms in autonomous driving. In contrast to previous approaches that…