ThermalSplat: Generalizable Pose-Free Gaussian Splatting for Sparse-View Thermal 3D Reconstruction

Thermal infrared imaging is vital for applications like search-and-rescue and autonomous navigation, yet trans- lating it into 3D scene representations remains a critical bottleneck. The low-texture nature and view-dependent emissivity of thermal images cause standard pose estima- tors to fail catastrophically, collapsing standard 3D Gaus- sian Splatting pipelines under sparse-view constraints. Fur- thermore, existing thermal reconstruction methods rely heavily on dense capture and computationally expensive per-scene optimization. Critically, many of these ap- proaches depend on aligned RGB-thermal datasets to ob- tain reliable camera poses and initial point clouds, ren- dering them unusable in purely thermal, unposed envi- ronments. To break this bottleneck, we present Thermal- Splat, the first generalizable, feed-forward Gaussian splat- ting framework that reconstructs 3D scenes directly from sparse thermal images without requiring known camera poses, RGB guidance, or per-scene training. We over- come thermal-specific challenges using a soft census loss for brightness-invariant structural matching and a bound- ary structure loss to explicitly supervise temperature discon- tinuities. Trained on an aggregated multi-source dataset, ThermalSplat synthesizes sharp reconstructions of unseen scenes in a single feed-forward pass. Extensive experi- ments demonstrate that ThermalSplat holds a strict advan- tage over scene-specific thermal splatting methods in the sparse regime. Furthermore, even compared against these baselines operating under their ideal dense capture set- tings, ThermalSplat achieves superior perceptual fidelity. By eliminating the need for computationally expensive per scene optimization, ThermalSplat offers an efficient and generalizable approach to thermal 3D reconstruction.