Abstract

Novel View Synthesis (NVS) for thermal infrared scenarios has become increasingly important in practical applications. Recent advances in 3D Gaussian Splatting (3DGS) have demonstrated its strong performance for NVS, and a handful of preliminary efforts have explored its extension to thermal NVS. However, existing 3DGS-based methods for thermal NVS often suffer from severe local blur in rendered images. Theoretical and empirical analyses reveal that the single-channel nature of thermal data allows intensity discrepancy to be compensated through opacity adjustment, which can erroneously amplify the opacity of floating Gaussians, especially when the background behind them is insufficiently represented. To address this issue, we propose a method termed Floating Gaussian Suppression (FGS) for thermal NVS. Specifically, we leverage an over-saturation of luminance to introduce an implicit gradient penalty, which guides the optimizer to suppress the opacity of floaters to match the ground truth. By effectively reducing the opacity of these floaters, this mechanism alleviates the occlusion of distant details, thereby mitigating local blur. Moreover, to preserve rendering fidelity, the luminance parameters of Gaussians are optimized concurrently. Experimental results demonstrate that our method effectively mitigates local blur and produces sharper and more faithful thermal reconstruction compared with the baseline methods.