6 papers
Computation-Aware Event-to-Frame Reconstruction via Selective Attention
Jingqian Wu, Yunbo Jia, Edmund Y. Lam
Event-to-frame (E2F) reconstruction bridges asynchronous event streams with frame-based vision pipelines, but existing methods often face a trade-off between reconstruction quality…
Dark-EvGS: Event Camera as an Eye for Radiance Field in the Dark
Jingqian Wu, Peiqi Duan, Zongqiang Wang +3
In low-light environments, conventional cameras often struggle to capture clear multi-view images of objects due to dynamic range limitations and motion blur caused by long exposur…
Semantic-E2VID: a Semantic-Enriched Paradigm for Event-to-Video Reconstruction
Jingqian Wu, Yunbo Jia, Shengpeng Xu +1
Event cameras provide a promising sensing modality for high-speed and high-dynamic-range vision by asynchronously capturing brightness changes. A fundamental task in event-based vi…
SweepEvGS: Event-Based 3D Gaussian Splatting for Macro and Micro Radiance Field Rendering from a Single Sweep
Jingqian Wu, Shuo Zhu, Chutian Wang +2
Recent advancements in 3D Gaussian Splatting (3D-GS) have demonstrated the potential of using 3D Gaussian primitives for high-speed, high-fidelity, and cost-efficient novel view sy…
Ev-GS: Event-based Gaussian splatting for Efficient and Accurate Radiance Field Rendering
Jingqian Wu, Shuo Zhu, Chutian Wang +1
Computational neuromorphic imaging (CNI) with event cameras offers advantages such as minimal motion blur and enhanced dynamic range, compared to conventional frame-based methods.…
Segment Anything Model is a Good Teacher for Local Feature Learning
Jingqian Wu, Rongtao Xu, Zach Wood-Doughty +3
Local feature detection and description play an important role in many computer vision tasks, which are designed to detect and describe keypoints in "any scene" and "any downstream…