PCDFusion is a new infrared-visible image fusion method that treats fusion as a local Bayesian posterior rendering problem. It uses a visible-anchored framework with three rendering stages to preserve useful thermal information, correct exposure-degraded visible areas, and incorporate reliable infrared structures into the final image. Experiments on public benchmarks demonstrate improved fusion quality and better performance on downstream detection and segmentation tasks under challenging lighting conditions.
arXiv:2603.16130v5 Announce Type: replace Abstract: Overexposure caused by strong daylight and oncoming headlights frequently overwhelms visible sensors, resulting in critical information loss in vis...
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:2607. 17611v1 Announce Type: cross Abstract: Multi-exposure fusion (MEF) expands the luminance range beyond what a single exposure can capture.
Mobile infrared-visible imaging typically pairs a compact infrared sensor with a high-resolution visible camera for complementary perception. While cross-sensor misalignment caused by different optics, viewpoints, fields of view, and exposure timings hinders practical deployment.
VesperMUSE is a multi‑modal vision transformer that enhances image semantic segmentation under varied lighting by combining physics‑informed low‑light adaptation, thermal‑guided low‑light image enhancement, and surface normal maps. The framework fuses RGB, thermal, and normal features through a modality‑agnostic self‑attention mechanism, enabling robust segmentation without architectural changes for different sensor setups. Extensive tests show that VesperMUSE recovers missing visual cues and achieves state‑of‑the‑art performance in extreme, unstructured environments.
arXiv:2604. 23094v2 Announce Type: replace-cross Abstract: Portrait relighting is a low-level vision problem in which physically plausible illumination transfer, identity preservation, and compact real-time inference must be considered together.
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.
RoES is a Rotational Equivariant Selective-frequency fusion network that dynamically separates low- and high-frequency components of infrared-visible images. It uses a trainable rotation-enhanced updater to decouple frequencies, then fuses them with a dual-branch module: a rotation-equivariant Mamba for low-frequency structural dependencies and a polar spectral attention Dual-Fourier block for high-frequency detail refinement. Experiments show RoES outperforms existing methods in fusion quality and downstream object detection, offering a robust multimodal fusion solution.
arXiv:2601.15897v3 Announce Type: replace Abstract: Multi-modal scene reconstruction integrating RGB and thermal infrared data is essential for robust environmental perception across diverse lighting...
arXiv:2606. 30215v1 Announce Type: cross Abstract: RGB-T detectors leverage the complementary strengths of visible and thermal infrared modalities, achieving robust performance under challenging conditions.
arXiv:2610.19847v1 Announce Type: new Abstract: We address inverse rendering with 3D Gaussian Splatting (3DGS) by introducing explicit visibility modeling for relighting as well as material and light...
The paper introduces FlexibleFusion, a method for infrared-visible object detection that adapts to both complete and missing-modality scenarios. It employs a Modality-Aware Experts Collaboration mechanism to selectively fuse cross-modal or intra-modal pathways, and a Residual Self-Paced Entropic Optimal Transport module to align heterogeneous feature distributions without heavy optimization. Experiments demonstrate consistent performance across various modality configurations.