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20242026
most citedL-MAGIC: Language Model Assisted Generation of Images with Coherence

2 citations · 4 across the 8 of their papers we have counts for

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10 papers · 1 filter

cs.CV2026

GemDepth: Geometry-Embedded Features for 3D-Consistent Video Depth

Yuecheng Liu, Junda Cheng, Longliang Liu +4

Video depth estimation extends monocular prediction into the temporal domain to ensure coherence. However, existing methods often suffer from spatial blurring in fine-detail region…

cs.CV2026

PromptStereo: Zero-Shot Stereo Matching via Structure and Motion Prompts

Xianqi Wang, Hao Yang, Hangtian Wang +4

Modern stereo matching methods have leveraged monocular depth foundation models to achieve superior zero-shot generalization performance. However, most existing methods primarily f…

cs.CV2025

PriOr-Flow: Enhancing Primitive Panoramic Optical Flow with Orthogonal View

Longliang Liu, Miaojie Feng, Junda Cheng +3

Panoramic optical flow enables a comprehensive understanding of temporal dynamics across wide fields of view. However, severe distortions caused by sphere-to-plane projections, suc…

cs.CV2025

A Wavelet-based Stereo Matching Framework for Solving Frequency Convergence Inconsistency

Xiaobao Wei, Jiawei Liu, Dongbo Yang +3

We find that the EPE evaluation metrics of RAFT-stereo converge inconsistently in the low and high frequency regions, resulting high frequency degradation (e.g., edges and thin obj…

cs.CV2025

BANet: Bilateral Aggregation Network for Mobile Stereo Matching

Gangwei Xu, Jiaxin Liu, Xianqi Wang +5

State-of-the-art stereo matching methods typically use costly 3D convolutions to aggregate a full cost volume, but their computational demands make mobile deployment challenging. D…

cs.CV2025

ZeroStereo: Zero-shot Stereo Matching from Single Images

Xianqi Wang, Hao Yang, Gangwei Xu +6

State-of-the-art supervised stereo matching methods have achieved remarkable performance on various benchmarks. However, their generalization to real-world scenarios remains challe…