activity
20212025
most citedSpatio-Temporal Recurrent Networks for Event-Based Optical Flow Estimation

9 citations · 19 across the 6 of their papers we have counts for

collaborators

6 papers

cs.CV2025

SpikeStereoNet: A Brain-Inspired Framework for Stereo Depth Estimation from Spike Streams

Zhuoheng Gao, Yihao Li, Jiyao Zhang +7

Conventional frame-based cameras often struggle with stereo depth estimation in rapidly changing scenes. In contrast, bio-inspired spike cameras emit asynchronous events at microse…

cs.CV2023★ 4 cited

Unsupervised Optical Flow Estimation with Dynamic Timing Representation for Spike Camera

Lujie Xia, Ziluo Ding, Rui Zhao +5

Efficiently selecting an appropriate spike stream data length to extract precise information is the key to the spike vision tasks. To address this issue, we propose a dynamic timin…

cs.CV2023★ 5 cited

WM-MoE: Weather-aware Multi-scale Mixture-of-Experts for Blind Adverse Weather Removal

Yulin Luo, Rui Zhao, Xiaobao Wei +7

Adverse weather removal tasks like deraining, desnowing, and dehazing are usually treated as separate tasks. However, in practical autonomous driving scenarios, the type, intensity…

cs.CV2023★ 1 cited

SpikeCV: Open a Continuous Computer Vision Era

Yajing Zheng, Jiyuan Zhang, Rui Zhao +5

SpikeCV is a new open-source computer vision platform for the spike camera, which is a neuromorphic visual sensor that has developed rapidly in recent years. In the spike camera, e…

cs.CV2021

Optical Flow Estimation for Spiking Camera

Liwen Hu, Rui Zhao, Ziluo Ding +4

As a bio-inspired sensor with high temporal resolution, the spiking camera has an enormous potential in real applications, especially for motion estimation in high-speed scenes. Ho…

cs.CV2021★ 9 cited

Spatio-Temporal Recurrent Networks for Event-Based Optical Flow Estimation

Ziluo Ding, Rui Zhao, Jiyuan Zhang +4

Event camera has offered promising alternative for visual perception, especially in high speed and high dynamic range scenes. Recently, many deep learning methods have shown great…