6 papers · 1 filter
Self-supervised Learning of Event-guided Video Frame Interpolation for Rolling Shutter Frames
Yunfan Lu, Guoqiang Liang, Yiran Shen +1
Most consumer cameras use rolling shutter (RS) exposure, which often leads to distortions such as skew and jelly effects. These videos are further limited by bandwidth and frame ra…
CompoNeRF: Text-guided Multi-object Compositional NeRF with Editable 3D Scene Layout
Haotian Bai, Yuanhuiyi Lyu, Lutao Jiang +4
Text-to-3D form plays a crucial role in creating editable 3D scenes for AR/VR. Recent advances have shown promise in merging neural radiance fields (NeRFs) with pre-trained diffusi…
EventBind: Learning a Unified Representation to Bind Them All for Event-based Open-world Understanding
Jiazhou Zhou, Xu Zheng, Yuanhuiyi Lyu +1
In this paper, we propose EventBind, a novel and effective framework that unleashes the potential of vision-language models (VLMs) for event-based recognition to compensate for the…
UniINR: Event-guided Unified Rolling Shutter Correction, Deblurring, and Interpolation
Yunfan LU, Guoqiang Liang, Yusheng Wang +2
Video frames captured by rolling shutter (RS) cameras during fast camera movement frequently exhibit RS distortion and blur simultaneously. Naturally, recovering high-frame-rate gl…
360 High-Resolution Depth Estimation via Uncertainty-aware Structural Knowledge Transfer
Zidong Cao, Hao Ai, Athanasios V. Vasilakos +1
To predict high-resolution (HR) omnidirectional depth map, existing methods typically leverage HR omnidirectional image (ODI) as the input via fully-supervised learning. However, i…
Deep Learning for Event-based Vision: A Comprehensive Survey and Benchmarks
Xu Zheng, Yexin Liu, Yunfan Lu +5
Event cameras are bio-inspired sensors that capture the per-pixel intensity changes asynchronously and produce event streams encoding the time, pixel position, and polarity (sign)…