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20232026
most citedA Survey on All-in-One Image Restoration: Taxonomy, Evaluation and Future Trends

51 citations · 69 across the 36 of their papers we have counts for

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Showing 2026 · cs.CVShow all

6 papers · 2 filters

cs.CV2026

RegVGGT: Sustainable Visual Geometry Grounding for Streaming via Regulated Memory

Hongbo Mao, Junjun Jiang, Youyu Chen +3

3D reconstruction from a lengthy video stream input poses a dilemma for feed-forward reconstruction models (FFRMs), that a whole-stream inference context cannot be retained under l…

cs.CV2026

ViP-Rig: Visual-Prompted Controllable Rigging

Zihan Qin, Mingze Sun, Yifan Mao +6

Rigging is inherently task-dependent because the same mesh may require different skeletons and deformation behaviors across animation tasks. In practice, artists often inspect an i…

cs.CV2026

RainDancer: RGB-Event Video Deraining with Rain-Oriented Spiking Dynamics

Kui Jiang, Runzhe Li, Zhaocheng Yu +3

Video deraining aims to recover clean visual content from rainy videos for reliable perception under adverse weather. Existing methods mainly rely on RGB sequences and temporal red…

cs.CV2026

Reliev3R: Relieving Feed-forward Reconstruction from Multi-View Geometric Annotations

Youyu Chen, Junjun Jiang, Yueru Luo +4

With recent advances, Feed-forward Reconstruction Models (FFRMs) have demonstrated great potential in reconstruction quality and adaptiveness to multiple downstream tasks. However,…

cs.CV2026

Focus-Scan-Refine: From Human Visual Perception to Efficient Visual Token Pruning

Enwei Tong, Yuanchao Bai, Yao Zhu +2

Vision-language models (VLMs) often generate massive visual tokens that greatly increase inference latency and memory footprint; while training-free token pruning offers a practica…

cs.CV2026

UDPNet: Unleashing Depth-based Priors for Robust Image Dehazing

Zengyuan Zuo, Junjun Jiang, Gang Wu +1

Image dehazing has witnessed significant advancements with the development of deep learning models. However, most existing methods focus solely on single-modal RGB features, neglec…