Guanghu Xie — accepted AI-conference papers

2025

DexMGNet: Multi-Mode Dexterous Grasping in Cluttered Scenes With Generative Models

Zongwu Xie, Guanghu Xie, Yang Liu, Yonglong Zhang, Baoshi Cao, Yiming Ji +2

RA-L 2025

Dexterous grasping is a crucial technique in humanoid robot manipulation. However, existing methods still fall short in effectively detecting dexterous grasps in cluttered environments. In this work, we propose DexMGNet, a novel multi-mode dexterous grasping framework designed for such challenging s

Cited by 1 Source ↗Scholar ↗

2024

NEDS-SLAM: A Neural Explicit Dense Semantic SLAM Framework Using 3D Gaussian Splatting

Yiming Ji, Yang Liu, Guanghu Xie, Boyu Ma, Zongwu Xie, Hong Liu

RA-L 2024

We propose NEDS-SLAM, a dense semantic SLAM system based on 3D Gaussian representation, that enables robust 3D semantic mapping, accurate camera tracking, and high-quality rendering in real-time. In the system, we propose a <bold xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www

Cited by 38 Source ↗Scholar ↗