👋 Bio

I am a third-year Ph.D. student at the School of Engineering in the Westlake University, in collaboration with Zhejiang University. My PhD advisor is Prof. Peidong Liu. Before that, I received my Bachelor and Master Degree from Huazhong University of Science and Technology respectively in 2019 and 2022.
Currently, I am a research intern at Ant Research.
My research interest lies in 3D vision (e.g. Neural Rendering, Dynamic Scene Understanding, 3D Point Tracking) and 3D/4D Multimodal LLMs.

🌟 News

08/2025: Our paper MBA-SLAM is accepted to TPAMI 2025! Thanks to all collaborators!
07/2024: Our papers BAD-Gaussians and BeNeRF are accepted to ECCV 2024! Thanks to all collaborators!
01/2024: Our paper USB-NeRF is accepted to ICLR 2024! Thanks to all collaborators!
02/2023: Our paper BAD-NeRF is accepted to CVPR 2023! Thanks to all collaborators!

📖 Publications

* denotes equal contribution or advising; † denotes corresponding author.

Styl3R: Instant 3D Stylized Reconstruction for Arbitrary Scenes and Styles
arXiv 2025
Peng Wang*, Xiang Liu*, Peidong Liu†

Styl3R predicts stylized 3D Gaussians in less than a second using a feed-forward network given unposed sparse-view images and an arbitrary style image.

MBA-SLAM: Motion Blur Aware Dense Visual SLAM with Radiance Fields Representation
TPAMI 2025

MBA-SLAM can accurately estimate the local camera motion trajectories within the exposure time and recovers the high quality 3D scene from blurry sequences.

BeNeRF: Neural Radiance Fields from a Single Blurry Image and Event Stream
ECCV 2024

We explore the possibility of recovering the neural radiance fields and camera motion trajectory from a single blurry image with its events.

BAD-Gaussians: Bundle Adjusted Deblur Gaussian Splatting
ECCV 2024

BAD-Gaussians successfully deblurs severe motion-blurred images, synthesizes higher-quality novel views, and achieves real-time rendering, surpassing previous SOTA implicit deblurring rendering methods.

SCL-VI: Self-supervised Context Learning for Visual Inspection of Industrial Defects
arXiv 2023
Peng Wang, Haiming Yao, Wenyong Yu†

We address the challenge of detecting object defects through the self-supervised learning approach of solving the jigsaw puzzle problem.

USB-NeRF: Unrolling Shutter Bundle Adjusted Neural Radiance Fields
ICLR 2024

USB-NeRF can remove rolling shutter effect and recover high-fidelity high frame-rate global shutter video from a sequence of rolling shutter images.

BAD-NeRF: Bundle Adjusted Deblur Neural Radiance Fields
CVPR 2023
Peng Wang, Lingzhe Zhao, Ruijie Ma, Peidong Liu†

BAD-NeRF jointly learns the 3D representation and optimizes the camera motion trajectories within exposure time from blurry images and inaccurate initial poses.
Nov. 2023 update: we have released the NeRFstudio version Bad-RFs.

🐥 Experience

Research Intern, Ant Research
Jul. 2025 - Now
Host by Nan Xue and Yinghao Xu

🎮 Projects

Bad-RFs: Bundle-adjusted Radiance Fields from Degraded Images with Continuous-time Motion Models
Research Project, 2023

BAD-NeRFstudio can shorten the training of BAD-NeRF to minutes.

🏅 Awards

  • Muyuan Fellowship (20000 CNY), Westlake University-Muyuan joint Research Institute, 2022
  • National Academic First Class Scholarship for Postgraduates, HUST, 2021, 2022
  • 7th in HUAWEI Software Elite Challenge (Huazhong Division), 2020
  • 3rd Prize in China Youth Cup National College Students Mathematical Modeling Competition, 2020
  • 1st Prize of the Robot Innovation Design Competition of HUST, 2019