Peng Yu
Current Organizations / Institutes
The organizations/institutes from the scholar's latest article
Peng Yu has a total of 6 co-authors. Here are the top co-authors by number of shared publications:
- Jun Wang
School of Information and Control Engineering, China University of Mining and Technology
1 shared publication Nuo Li
School of Information and Control Engineering, China University of Mining and Technology, Xuzhou, China
1 shared publication- Qi Wang
School of Information and Control Engineering, China University of Mining and Technology, Xuzhou, China
1 shared publication - Shaoze You
School of Information and Control Engineering, China University of Mining and Technology, Xuzhou 221116, China
1 shared publication Yongqiang Qi
School of Mathematics, China University of Mining and Technology, Xuzhou, China
1 shared publication- Yuchen Weng
School of Information and Control Engineering, China University of Mining and Technology, Xuzhou, China
1 shared publication
Latest Publications
- 12 January 2026
- Published by Institute of Electrical and Electronics Engineers (IEEE) in IEEE Robotics and Automation Letters
- Vol. 11 (3) , 2474-2481
EiGS: Event-Informed 3D Deblur Reconstruction With Gaussian Splatting
Neural Radiance Fields (NeRF) have significantly advanced photorealistic novel view synthesis. Recently, 3D Gaussian Splatting has emerged as a promising technique with faster training and rendering speeds. However, both methods rely heavily on clear images and precise camera poses, limiting performance under motion blur. To address this, we introduce Event-Informed 3D Deblur Reconstruction with Gaussian Splatting(EiGS), a novel approach leveraging event camera data to enhance 3D Gaussian Splatting, improving sharpness and clarity in scenes affected by motion blur. Our method employs an Adaptive Deviation Estimator to learn Gaussian center shifts as the inverse of complex camera jitter, enabling simulation of motion blur during training. A motion consistency loss ensures global coherence in Gaussian displacements, while Blurriness and Event Integration Losses guide the model toward precise 3D representations. Extensive experiments demonstrate superior sharpness and real-time rendering capabilities compared to existing methods, with ablation studies validating the effectiveness of our components in robust, high-quality reconstruction for complex static scenes.
- 12 January 2026
- Published by Institute of Electrical and Electronics Engineers (IEEE) in IEEE Robotics and Automation Letters
- Vol. 11 (3) , 2474-2481
EiGS: Event-Informed 3D Deblur Reconstruction With Gaussian Splatting
Neural Radiance Fields (NeRF) have significantly advanced photorealistic novel view synthesis. Recently, 3D Gaussian Splatting has emerged as a promising technique with faster training and rendering speeds. However, both methods rely heavily on clear images and precise camera poses, limiting performance under motion blur. To address this, we introduce Event-Informed 3D Deblur Reconstruction with Gaussian Splatting(EiGS), a novel approach leveraging event camera data to enhance 3D Gaussian Splatting, improving sharpness and clarity in scenes affected by motion blur. Our method employs an Adaptive Deviation Estimator to learn Gaussian center shifts as the inverse of complex camera jitter, enabling simulation of motion blur during training. A motion consistency loss ensures global coherence in Gaussian displacements, while Blurriness and Event Integration Losses guide the model toward precise 3D representations. Extensive experiments demonstrate superior sharpness and real-time rendering capabilities compared to existing methods, with ablation studies validating the effectiveness of our components in robust, high-quality reconstruction for complex static scenes.