Physically Grounded Dual-Opacity Gaussian Splatting for Joint RGB-TIR Reconstruction

3D Gaussian Splatting (3DGS) enables efficient novel-viewsynthesis, yet its extension to simultaneous RGB and thermal infrared(TIR) reconstruction is hindered by a fundamental spectral mismatch:RGB relies on reflected illumination while TIR captures emitted radi-ance. This disparity in frequency characteristics and physical sensingmechanisms causes gradient interference when naively sharing a unifiedrepresentation. Common methods typically treat TIR as an auxiliary in-tensity channel, ignoring the physical radiative transfer governing ther-mal imaging. Critically, enforcing a single opacity field across modalitiesis physically invalid due to cross-spectral visibility disparities, constrain-ing representational capacity and yielding thermally implausible recon-structions. To address these limitations, we propose Physically GroundedDual-Opacity Gaussian Splatting, a framework that unifies RGB andTIR reconstruction under a shared geometric scaffold with modality-aware visibility modeling. We introduce Radiative Attribute Parameter-ization, explicitly modeling each Gaussian’s thermal response throughemissivity, temperature, and reflectance. Dual-Opacity Rendering en-ables spectrally aware visibility handling by assigning modality-specificopacities to shared location parameters, resolving cross-modal occlusionconflicts. Training jointly optimizes photometric reconstruction, physicalpriors, and RGB-guided geometric regularization to resolve parameterambiguities. Experiments demonstrate competitive performance in bothradiometric accuracy and photorealistic novel view synthesis, highlight-ing the potential of 3DGS for joint thermal field with high-fidelity RGBreconstruction.