5 papers · 1 filter
Unsupervised Point Cloud Registration via Training-Time Semantic Guidance
Kezheng Xiong, Shiyun Xu, Sheng Ao +3
Unsupervised registration of large-scale LiDAR point clouds remains challenging due to the geometric ambiguity inherent in outdoor scenes, which degrades pseudo-label quality and l…
LightLoc++: Sensor-Robust Representation Learning for Efficient Outdoor LiDAR Localization
Wen Li, Shangshu Yu, Dunqiang Liu +5
Scene coordinate regression (SCR) achieves strong performance in outdoor LiDAR localization, but it usually requires scene-specific training that can take days, limiting practical…
LEADER: Learning Reliable Local-to-Global Correspondences for LiDAR Relocalization
Jianshi Wu, Minghang Zhu, Dunqiang Liu +5
LiDAR relocalization has attracted increasing attention as it can deliver accurate 6-DoF pose estimation in complex 3D environments. Recent learning-based regression methods offer…
From Single Scan to Sequential Consistency: A New Paradigm for LIDAR Relocalization
Minghang Zhu, Zhijing Wang, Yuxin Guo +3
LiDAR relocalization aims to estimate the global 6-DoF pose of a sensor in the environment. However, existing regression-based approaches are prone to dynamic or ambiguous scenario…
MoniRefer: A Real-world Large-scale Multi-modal Dataset based on Roadside Infrastructure for 3D Visual Grounding
Panquan Yang, Junfei Huang, Zongzhangbao Yin +9
3D visual grounding aims to localize the object in 3D point cloud scenes that semantically corresponds to given natural language sentences. It is very critical for roadside infrast…