3 papers
cs.CV2025
DcMatch: Unsupervised Multi-Shape Matching with Dual-Level Consistency
Tianwei Ye, Yong Ma, Xiaoguang Mei
Establishing point-to-point correspondences across multiple 3D shapes is a fundamental problem in computer vision and graphics. In this paper, we introduce DcMatch, a novel unsuper…
cs.CV2025
Selecting and Pruning: A Differentiable Causal Sequentialized State-Space Model for Two-View Correspondence Learning
Xiang Fang, Shihua Zhang, Hao Zhang +3
Two-view correspondence learning aims to discern true and false correspondences between image pairs by recognizing their underlying different information. Previous methods either t…
cs.CV2025
SpecDM: Hyperspectral Dataset Synthesis with Pixel-level Semantic Annotations
Wendi Liu, Pei Yang, Wenhui Hong +2
In hyperspectral remote sensing field, some downstream dense prediction tasks, such as semantic segmentation (SS) and change detection (CD), rely on supervised learning to improve…