AnyThermalSplat: Scaling Pose-Free Feed-Forward Thermal Scene Reconstruction

Thermal 3D reconstruction typically relies on known cameras and per-scene optimization, while the scarcity of multi-view thermal data hinders scalable feed-forward reconstruction. We present AnyThermalSplat, a pose-free feed-forward framework that predicts renderable thermal Gaussian scenes without external camera parameters or test-time scene optimization. It adapts RGB-pretrained geometric priors through low-rank updates and residual feature bridges, with a frozen RGB teacher providing geometric supervision alongside thermal reconstruction and held-out novel-view objectives; inference requires thermal images only. To expand supervision, we introduce ThermalVerse, a multi-domain RGB–thermal dataset, and a geometry-grounded thermalization pipeline that fits shared thermal appearance on fixed RGB-reconstructed geometry. Together with curated public data, these resources form a 500-scene training pool. On in-domain scenes, AnyThermalSplat achieves 26.76 and 28.48 dB PSNR with 6 and 12 context views, respectively, outperforming our matched thermally adapted AnySplat-IR baseline by 2.84 and 1.77 dB. This baseline is trained on the same thermal data under the same adaptation protocol, providing a controlled comparison beyond directly applying the original RGB model. We also conduct a controlled scaling study on nested 100–500-scene subsets under a fixed update budget and find substantial gains in both in-domain and out-of-domain reconstruction as the training set grows from 100 to 300 scenes. Beyond this regime, out-of-domain performance largely saturates, while in-domain reconstruction retains an overall benefit from larger training sets. We will release the data and code, along with model checkpoints.