7 papers · 1 filter
LargeAD: Large-Scale Cross-Sensor Data Pretraining for Autonomous Driving
Lingdong Kong, Xiang Xu, Youquan Liu +6
Recent advancements in vision foundation models (VFMs) have revolutionized visual perception in 2D, yet their potential for 3D scene understanding, particularly in autonomous drivi…
SPIRAL: Semantic-Aware Progressive LiDAR Scene Generation and Understanding
Dekai Zhu, Yixuan Hu, Youquan Liu +3
Leveraging recent diffusion models, LiDAR-based large-scale 3D scene generation has achieved great success. While recent voxel-based approaches can generate both geometric structur…
OVGaussian: Generalizable 3D Gaussian Segmentation with Open Vocabularies
Runnan Chen, Xiangyu Sun, Zhaoqing Wang +8
Open-vocabulary scene understanding using 3D Gaussian (3DGS) representations has garnered considerable attention. However, existing methods mostly lift knowledge from large 2D visi…
Learning to Adapt SAM for Segmenting Cross-domain Point Clouds
Xidong Peng, Runnan Chen, Feng Qiao +6
Unsupervised domain adaptation (UDA) in 3D segmentation tasks presents a formidable challenge, primarily stemming from the sparse and unordered nature of point cloud data. Especial…
An Empirical Study of Training State-of-the-Art LiDAR Segmentation Models
Jiahao Sun, Chunmei Qing, Xiang Xu +10
In the rapidly evolving field of autonomous driving, precise segmentation of LiDAR data is crucial for understanding complex 3D environments. Traditional approaches often rely on d…
OpenESS: Event-based Semantic Scene Understanding with Open Vocabularies
Lingdong Kong, Youquan Liu, Lai Xing Ng +2
Event-based semantic segmentation (ESS) is a fundamental yet challenging task for event camera sensing. The difficulties in interpreting and annotating event data limit its scalabi…