3 citations · 4 across the 3 of their papers we have counts for
4 papers · 1 filter
Efficient and Robust Absolute Pose Estimation via Gravity-Prior-Driven Transformation Decoupling and Pose Refinement
Hu Cao, Qianyi Yang, Xinyi Li +3
Estimation of the absolute pose of an object is an essential task for various robotic applications. Recently, incorporating gravity direction as prior information has emerged as a…
Embracing Events and Frames with Hierarchical Feature Refinement Network for Object Detection
Hu Cao, Zehua Zhang, Yan Xia +4
In frame-based vision, object detection faces substantial performance degradation under challenging conditions due to the limited sensing capability of conventional cameras. Event…
Transformation Decoupling Strategy based on Screw Theory for Deterministic Point Cloud Registration with Gravity Prior
Xinyi Li, Zijian Ma, Yinlong Liu +4
Point cloud registration is challenging in the presence of heavy outlier correspondences. This paper focuses on addressing the robust correspondence-based registration problem with…
Efficient and Deterministic Search Strategy Based on Residual Projections for Point Cloud Registration with Correspondences
Xinyi Li, Hu Cao, Yinlong Liu +3
Estimating the rigid transformation between two LiDAR scans through putative 3D correspondences is a typical point cloud registration paradigm. Current 3D feature matching approach…