5 papers
Scene Reconstruction as Mapping Priors for 3D Detection
Yang Fu, Yuliang Zou, Hao Xiang +8
In autonomous driving, mapping is critical for motion planning but remains an under-utilized resource for perception tasks such as 3D object detection. Maps can provide robust stru…
STELLAR: Scaling 3D Perception Large Models for Autonomous Driving
Yingwei Li, Xin Huang, Yang Liu +13
Model scaling has demonstrated remarkable success through large-scale training on diverse datasets. It remains an open question whether the same paradigm would apply to autonomous…
V2U4Real: A Real-world Large-scale Dataset for Vehicle-to-UAV Cooperative Perception
Weijia Li, Haoen Xiang, Tianxu Wang +4
Modern autonomous vehicle perception systems are often constrained by occlusions, blind spots, and limited sensing range. While existing cooperative perception paradigms, such as V…
Learning to Detect Objects from Multi-Agent LiDAR Scans without Manual Labels
Qiming Xia, Wenkai Lin, Haoen Xiang +5
Unsupervised 3D object detection serves as an important solution for offline 3D object annotation. However, due to the data sparsity and limited views, the clustering-based label f…
Mining and Transferring Feature-Geometry Coherence for Unsupervised Point Cloud Registration
Kezheng Xiong, Haoen Xiang, Qingshan Xu +4
Point cloud registration, a fundamental task in 3D vision, has achieved remarkable success with learning-based methods in outdoor environments. Unsupervised outdoor point cloud reg…