26 citations · 28 across the 6 of their papers we have counts for
7 papers · 1 filter
TOL: Textual Localization with OpenStreetMap
Youqi Liao, Shuhao Kang, Jingyu Xu +6
Natural language provides an intuitive way to express spatial intent in geospatial applications. While existing localization methods often rely on dense point cloud maps or high-re…
Certainty Is Redundant: Token Sparsification for Efficient Camouflaged Object Detection with Vision Foundation Models
Yuhan Gao, Shuhao Kang, Xin He +3
Camouflaged object detection (COD) aims to segment objects that closely resemble their surrounding environments. Vision foundation models (VFMs) provide strong transferable represe…
VLM-Loc: Localization in Point Cloud Maps via Vision-Language Models
Shuhao Kang, Youqi Liao, Peijie Wang +5
Text-to-point-cloud (T2P) localization aims to infer precise spatial positions within 3D point cloud maps from natural language descriptions, reflecting how humans perceive and com…
OPAL: Visibility-aware LiDAR-to-OpenStreetMap Place Recognition via Adaptive Radial Fusion
Shuhao Kang, Martin Y. Liao, Yan Xia +3
LiDAR place recognition is a critical capability for autonomous navigation and cross-modal localization in large-scale outdoor environments. Existing approaches predominantly depen…
OSMLoc: Single Image-Based Visual Localization in OpenStreetMap with Fused Geometric and Semantic Guidance
Youqi Liao, Xieyuanli Chen, Shuhao Kang +4
OpenStreetMap (OSM), a rich and versatile source of volunteered geographic information (VGI), facilitates human self-localization and scene understanding by integrating nearby visu…
Mobile-Seed: Joint Semantic Segmentation and Boundary Detection for Mobile Robots
Youqi Liao, Shuhao Kang, Jianping Li +5
Precise and rapid delineation of sharp boundaries and robust semantics is essential for numerous downstream robotic tasks, such as robot grasping and manipulation, real-time semant…
CoFiI2P: Coarse-to-Fine Correspondences for Image-to-Point Cloud Registration
Shuhao Kang, Youqi Liao, Jianping Li +7
Image-to-point cloud (I2P) registration is a fundamental task for robots and autonomous vehicles to achieve cross-modality data fusion and localization. Current I2P registration me…