SyncGS: Self-Aligning Dynamic RGB and Thermal Gaussian Splatting

Dynamic reconstruction from RGB and Thermal streams benefits from their complementary appearance and thermal cues. In practice, however, independently recorded streams often exhibit residual temporal offsets and viewpoint discrepancies, which complicate reliable cross-modal reconstruction. We present SyncGS, a self-aligning framework for dynamic Gaussian reconstruction from RGB and Thermal streams. SyncGS uses cross-modal motion cues to build a motion cost volume with local spatial tolerance and continuously queries this volume to estimate a sequence-level temporal offset. During optimization, Thermal image supervision uses the current temporal estimate to learn an effective spatial correction shared across the sequence under a shared virtual-camera model. To limit mutual compensation between calibration and scene variables, we introduce Observable-Owned Optimization, which assigns motion, image-alignment, and reconstruction objectives to the temporal, spatial, and scene variables, respectively, while retaining a shared forward state. Experiments on DynamicRGBT-Scenes show improved reconstruction quality in both RGB and Thermal modalities, recovery of imposed temporal offsets, and resilience to controlled pose-initialization perturbations.