2 citations · 3 across the 6 of their papers we have counts for
9 papers
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…
MagicFuse: Single Image Fusion for Visual and Semantic Reinforcement
Hao Zhang, Yanping Zha, Zizhuo Li +2
This paper focuses on a highly practical scenario: how to continue benefiting from the advantages of multi-modal image fusion under harsh conditions when only visible imaging senso…
DistillMatch: Leveraging Knowledge Distillation from Vision Foundation Model for Multimodal Image Matching
Meng Yang, Fan Fan, Zizhuo Li +3
Multimodal image matching seeks pixel-level correspondences between images of different modalities, crucial for cross-modal perception, fusion and analysis. However, the significan…
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…