40 citations · 42 across the 7 of their papers we have counts for
6 papers · 1 filter
U4D: Uncertainty-Aware 4D World Modeling from LiDAR Sequences
Xiang Xu, Alan Liang, Youquan Liu +4
Modeling dynamic 3D environments from LiDAR sequences is central to building reliable 4D worlds for autonomous driving and embodied AI. Existing generative frameworks, however, oft…
Beyond One Shot, Beyond One Perspective: Cross-View and Long-Horizon Distillation for Better LiDAR Representations
Xiang Xu, Lingdong Kong, Song Wang +2
LiDAR representation learning aims to extract rich structural and semantic information from large-scale, readily available datasets, reducing reliance on costly human annotations.…
Enhanced Spatiotemporal Consistency for Image-to-LiDAR Data Pretraining
Xiang Xu, Lingdong Kong, Hui Shuai +5
LiDAR representation learning has emerged as a promising approach to reducing reliance on costly and labor-intensive human annotations. While existing methods primarily focus on sp…
LiMoE: Mixture of LiDAR Representation Learners from Automotive Scenes
Xiang Xu, Lingdong Kong, Hui Shuai +3
LiDAR data pretraining offers a promising approach to leveraging large-scale, readily available datasets for enhanced data utilization. However, existing methods predominantly focu…
4D Contrastive Superflows are Dense 3D Representation Learners
Xiang Xu, Lingdong Kong, Hui Shuai +5
In the realm of autonomous driving, accurate 3D perception is the foundation. However, developing such models relies on extensive human annotations -- a process that is both costly…
FRNet: Frustum-Range Networks for Scalable LiDAR Segmentation
Xiang Xu, Lingdong Kong, Hui Shuai +1
LiDAR segmentation has become a crucial component of advanced autonomous driving systems. Recent range-view LiDAR segmentation approaches show promise for real-time processing. How…