Scene coordinate regression (SCR) achieves strong performance in outdoor LiDAR localization, but it usually requires scene-specific training that can take days, limiting practical deployment. Recent works improve training efficiency by decoupling SCR into a scene-agnostic backbone and scene-specific prediction heads, w...
Wen Li, Shang-Shu Yu, Dunqiang Liu et al.· 0 citations
LiDAR relocalization aims to estimate the global 6-DoF pose of a sensor in the environment. However, existing regression-based approaches often encounter limitations in dynamic or ambiguous scenarios, as they typically prioritize single-frame inference, leaving the potential of spatio-temporal consistency across scans...
Ming-Hang Zhu, Zhi-Jing Wang, Yu-Xin Guo et al.· 0 citations
Cross-view geo-localization (CVGL) aims at localizing a ground-level query by retrieving its corresponding match from a database of geo-tagged satellite images. Existing multi-modal CVGL methods lack a structured design in the fusion stage, limiting their ability to fully exploit the information from multiple modalitie...
Xu Yan, Xiao-Ran Zhang, Zi-Wei Shi et al.· Proceedings of the Thirty-Fi...· 0 citations
CAESAR, a teacher-student framework guided by an off-the-shelf 3D segmentation model exclusively during training, is proposed and Semantic Predictive Distillation is introduced to consolidate the student's semantic awareness in the feature space.
Kezheng Xiong, Shi-Yun Xu, Sheng Ao et al.· 0 citations
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