8 citations · 9 across the 5 of their papers we have counts for
10 papers · 1 filter
AnyMatch: Supercharging Universal Multi-Modal Image Matching with Large-Scale Single-View Images
Meng Yang, Zizhuo Li, Linfeng Tang +2
Multi-modal image matching is essential for visual localization and multi-sensor fusion, but it is hindered by the scarcity of large-scale training data with precise geometric anno…
Efficient Sparse-to-Dense Visual Localization via Compact Gaussian Scene Representation and Accelerated Dense Pose Estimation
Zizhuo Li, Songchu Deng, Linfeng Tang +1
This letter presents LiteLoc, a novel and efficient localizer built on 3D Gaussian Splatting (3DGS). The previous state-of-the-art (SoTA) sparse-to-dense localizer, STDLoc, has sho…
TemCoCo: Temporally Consistent Multi-modal Video Fusion with Visual-Semantic Collaboration
Meiqi Gong, Hao Zhang, Xunpeng Yi +2
Existing multi-modal fusion methods typically apply static frame-based image fusion techniques directly to video fusion tasks, neglecting inherent temporal dependencies and leading…
Deep Learning Reforms Image Matching: A Survey and Outlook
Shihua Zhang, Zizhuo Li, Kaining Zhang +5
Image matching, which establishes correspondences between two-view images to recover 3D structure and camera geometry, serves as a cornerstone in computer vision and underpins a wi…
CoMatch: Dynamic Covisibility-Aware Transformer for Bilateral Subpixel-Level Semi-Dense Image Matching
Zizhuo Li, Yifan Lu, Linfeng Tang +2
This prospective study proposes CoMatch, a novel semi-dense image matcher with dynamic covisibility awareness and bilateral subpixel accuracy. Firstly, observing that modeling cont…
DSPFusion: Image Fusion via Degradation and Semantic Dual-Prior Guidance
Linfeng Tang, Chunyu Li, Guoqing Wang +2
Existing fusion methods are tailored for high-quality images but struggle with degraded images captured under harsh circumstances, thus limiting the practical potential of image fu…