SupIR-GS: Thermal Infrared Super-Resolution Novel View Synthesis with Imaging-Calibrated 3D Gaussian Splatting

Thermal infrared (TIR) imaging facilitates perception, se-curity surveillance, and industrial inspection. Compared with visiblespectrum, TIR observations are typically resolution-limited and texture-scarce. More critically, thermal diffusion smooths true geometric bound-aries into low-frequency blur, whereas non-uniform sensor response andfixed-pattern noise introduce structured high-frequency artifacts, yield-ing a pronounced signal-to-noise inversion. Consequently, reconstructinga high-resolution (HR) 3D TIR scene and synthesizing high-quality novelviews from only low-resolution (LR) infrared inputs remains challeng-ing. To address this, we propose the first imaging-calibrated TIR super-resolution 3D Gaussian Splatting (3DGS) framework that reconstructshigh-resolution 3D TIR scenes using only LR inputs. Specifically, we ex-plicitly model infrared imaging degradation on the clean radiation signalsrendered by 3DGS using physics-informed priors, enabling decoupling ofthe underlying thermal radiation from sensor-induced artifacts. To allevi-ate texture scarcity, an intensity-conditioned tone adapter is designed toapply local affine residual modulation, thereby surfacing latent thermalgradients. Finally, we implement a frequency-aware curriculum learn-ing strategy that leverages edge priors from a super-resolution teachernetwork, and employs a cosine ramp-up schedule to smoothly shift theoptimization focus from coarse low-frequency structures to fine high-frequency details. Extensive evaluations across TIR benchmarks showthat our method significantly outperforms existing works in both high-resolution synthesis quality and quantitative temperature fidelity.