8 papers
LiAuto-GeoX: Efficient Grounded Driving Transformer
Jiawei Lian, Haoyi Sun, Yang Wu +8
Dense 3D reconstruction has demonstrated immense potential for spatial understanding, yet its viability as a real-time, onboard representation for autonomous driving remains an ope…
OmniLiDAR: A Unified Diffusion Framework for Multi-Domain 3D LiDAR Generation
Youquan Liu, Weidong Yang, Ao Liang +9
LiDAR scene generation is increasingly important for scalable simulation and synthetic data creation, especially under diverse sensing conditions that are costly to capture at scal…
GEM: Generating LiDAR World Model via Deformable Mamba
Yang Wu, Zhaojiang Liu, Qiang Meng +5
World models, which simulate environmental dynamics and generate sensor observations, are gaining increasing attention in autonomous driving. However, progress in LiDAR-based world…
Learning to Factorize and Adapt: A Versatile Approach Toward Universal Spatio-Temporal Foundation Models
Siru Zhong, Junjie Qiu, Yangyu Wu +7
Spatio-Temporal (ST) Foundation Models (STFMs) promise cross-dataset generalization, yet joint ST pretraining is computationally expensive and grapples with the heterogeneity of do…
A Self-Conditioned Representation Guided Diffusion Model for Realistic Text-to-LiDAR Scene Generation
Wentao Qu, Guofeng Mei, Yang Wu +3
Text-to-LiDAR generation can customize 3D data with rich structures and diverse scenes for downstream tasks. However, the scarcity of Text-LiDAR pairs often causes insufficient tra…
3D and 4D World Modeling: A Survey
Lingdong Kong, Wesley Yang, Yu Yang +21
World modeling has become a cornerstone in AI research, enabling agents to understand, represent, and predict the dynamic environments they inhabit. While prior work largely emphas…