arXiv Computer Vision
SeeRF: Learning to See Beyond Fields of View via Generative Image Registration and Fusion
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arXiv Computer Vision
Sep 21
XCalib Depth-Guided Geometric Optimization for Dense Thermal-Visible Video Registration
XCalib is an unsupervised dense registration framework that aligns thermal and visible video streams by optimizing virtual pinhole camera parameters and predicted monocular depth, thereby restricting spatial displacements to physically valid projection geometries. It introduces a novel registration paradigm using camera parameterization as an implicit regularizer, a robust Normalized Edges Correlation (NEC) metric for cross‑spectral alignment, and demonstrates superior temporal stability and alignment accuracy on public ADAS datasets compared to unconstrained dense flow baselines.
arXiv Computer Vision
Sep 24
Diff-RF: Mutually Reinforced Image Registration and Fusion via Degradation-Aware Learning
Diff‑RF is a diffusion‑based framework that jointly performs image registration and fusion while accounting for degradation in multi‑modal images. It first restores modality‑specific degradations within each image, then uses a cross‑modal diffusion module that couples registration and fusion, refining alignment and enhancing complementary information. Experiments on extended datasets show that this coupled approach yields higher registration accuracy and fusion quality under diverse degraded conditions.
Hugging Face Trending Papers
Aug 13
P2Fusion: Prompt-based Progressive Infrared-Visible Image Fusion via Dual-Prior Distillation
Infrared-visible image fusion (IVIF) is pivotal for multimodal perception, yet reconciling the inherent information disparity between thermal and textural features remains a fundamental challenge. Existing prior-guided methods often rely on static constraints that induce optimization conflicts or utilize extrinsic semantic priors from large-scale foundation models (e.
arXiv Machine Learning
Jul 28
BeyondFusion: Self-Aligned Latent Diffusion for Calibration-Free Infrared Super-Resolution and Infrared-Visible Fusion
arXiv:2607. 24110v1 Announce Type: cross Abstract: Mobile infrared-visible imaging typically pairs a compact infrared sensor with a high-resolution visible camera for complementary perception.
arXiv Computer Vision
21h ago
FlowMoDE: Coarse-to-fine Flow Matching for Detail-aware Sim-to-Real Monocular Depth Estimation
FlowMoDE introduces a coarse‑to‑fine flow‑matching framework for monocular depth estimation that balances efficiency and detail reconstruction. The method first decodes coarse depth conditioned on diffused features from a pre‑trained semantic encoder, then refines fine structural patterns with expanded decoding layers. Trained only on a synthetic indoor dataset, FlowMoDE achieves a 14.1% lower relative error and a 9.9% lower Chamfer distance compared to state‑of‑the‑art methods across five real‑world indoor and outdoor datasets, and demonstrates strong generalization to realistic robotic scenes.
arXiv Computer Vision
Sep 1
UniDiffFusion: A Unified Diffusion Framework for Multi-Task and Degradation-Robust Image Fusion
arXiv:2608.21786v2 Announce Type: replace Abstract: General image fusion aims to integrate complementary information from multiple source images, but existing methods often rely on task-specific mode...