16 citations · 19 across the 7 of their papers we have counts for
7 papers
SmokeGS-R: Physics-Guided Pseudo-Clean 3DGS for Real-World Multi-View Smoke Restoration
Xueming Fu, Lixia Han
Real-world smoke simultaneously attenuates scene radiance, adds airlight, and destabilizes multi-view appearance consistency, making robust 3D reconstruction particularly difficult…
RadGS-Reg: Registering Spine CT with Biplanar X-rays via Joint 3D Radiative Gaussians Reconstruction and 3D/3D Registration
Ao Shen, Xueming Fu, Junfeng Jiang +6
Computed Tomography (CT)/X-ray registration in image-guided navigation remains challenging because of its stringent requirements for high accuracy and real-time performance. Tradit…
Dyna3DGR: 4D Cardiac Motion Tracking with Dynamic 3D Gaussian Representation
Xueming Fu, Pei Wu, Yingtai Li +6
Accurate analysis of cardiac motion is crucial for evaluating cardiac function. While dynamic cardiac magnetic resonance imaging (CMR) can capture detailed tissue motion throughout…
3DGR-CAR: Coronary artery reconstruction from ultra-sparse 2D X-ray views with a 3D Gaussians representation
Xueming Fu, Yingtai Li, Fenghe Tang +4
Reconstructing 3D coronary arteries is important for coronary artery disease diagnosis, treatment planning and operation navigation. Traditional reconstruction techniques often req…
HySparK: Hybrid Sparse Masking for Large Scale Medical Image Pre-Training
Fenghe Tang, Ronghao Xu, Qingsong Yao +5
The generative self-supervised learning strategy exhibits remarkable learning representational capabilities. However, there is limited attention to end-to-end pre-training methods…
3DGR-CT: Sparse-View CT Reconstruction with a 3D Gaussian Representation
Yingtai Li, Xueming Fu, Han Li +3
Sparse-view computed tomography (CT) reduces radiation exposure by acquiring fewer projections, making it a valuable tool in clinical scenarios where low-dose radiation is essentia…
Gait Cycle-Inspired Learning Strategy for Continuous Prediction of Knee Joint Trajectory from sEMG
Xueming Fu, Hao Zheng, Luyan Liu +9
Predicting lower limb motion intent is vital for controlling exoskeleton robots and prosthetic limbs. Surface electromyography (sEMG) attracts increasing attention in recent years…