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Wenpu Li

3 accepted papers

2025

E-MoFlow: Learning Egomotion and Optical Flow from Event Data via Implicit Regularization

NeurIPS 2025poster

The estimation of optical flow and 6-DoF ego-motion—two fundamental tasks in 3-D vision—has typically been addressed independently. For neuromorphic vision (e.g., event cameras), however, the lack of robust data association makes solving the two problems separately an ill-posed challenge, especiall…

Cited by 0SourceScholar
2025

SIU3R: Simultaneous Scene Understanding and 3D Reconstruction Beyond Feature Alignment

NeurIPS 2025spotlight

Simultaneous understanding and 3D reconstruction plays an important role in developing end-to-end embodied intelligent systems. To achieve this, recent approaches resort to 2D-to-3D feature alignment paradigm, which leads to limited 3D understanding capability and potential semantic information loss…

Cited by 0SourcecodeScholar
2024

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

ECCV 2024poster

"Implicit scene representation has attracted a lot of attention in recent research of computer vision and graphics. Most prior methods focus on how to reconstruct 3D scene representation from a set of images. In this work, we demonstrate the possibility to recover the neural radiance fields (NeRF) f…