S³-TIR: Coupled Spatial-Spectral Splatting for Thermal Infrared Novel View Synthesis
Proceedings of the Thirty-Fifth International Joint Conference on Artificial Intelligence. Main Track. Pages 1785-1793.
https://doi.org/10.24963/ijcai.2026/199
Keywords
- Computer Vision: 3D computer vision
- Computer Vision: Scene analysis and understanding
Abstract
We present S³-TIR (Coupled Spatial-Spectral Splatting), a physics-inspired framework for high-fidelity thermal infrared (TIR) novel view synthesis. Existing 3D Gaussian splatting methods struggle to capture the unique duality of TIR imagery: the coexistence of diffusive smoothness from heat conduction and structural sharpness from material discontinuities. To address this issue, we leverage the intrinsic frequency properties of thermal scenes, where conduction creates smooth fields while discontinuities cause sharp signal jumps. Accordingly, S³-TIR couples adaptive spatial support with internal spectral modulation via a unified generalized-exponential Gabor primitive. This design utilizes spatial support as a physical truncation controller, confining high-frequency textures within sharp boundaries while maintaining smooth diffusion elsewhere. Furthermore, we propose a frequency-aware deferred splatting scheme that enforces multi-view frequency consistency, anchoring frequency distributions to the 3D structure rather than view-dependent appearance. Experiments on diverse benchmarks demonstrate that S³-TIR achieves state-of-the-art performance, notably reducing LPIPS by 42.5% and improving PSNR by 3.2% on the RGBT-Scenes dataset, while yielding structurally stable thermal sequences free from jittering artifacts.
@inproceedings{ijcai2026p199,
title = {S³-TIR: Coupled Spatial-Spectral Splatting for Thermal Infrared Novel View Synthesis},
author = {Wang, Yaoxing and Li, Yuheng and Di, Yan and Gao, Shan},
booktitle = {Proceedings of the Thirty-Fifth International Joint Conference on
Artificial Intelligence, {IJCAI-26}},
publisher = {International Joint Conferences on Artificial Intelligence Organization},
editor = {Diego Calvanese},
pages = {1785--1793},
year = {2026},
month = {8},
note = {Main Track},
doi = {10.24963/ijcai.2026/199},
url = {https://doi.org/10.24963/ijcai.2026/199},
}
TY - CPAPER TI - S³-TIR: Coupled Spatial-Spectral Splatting for Thermal Infrared Novel View Synthesis AU - Wang, Yaoxing AU - Li, Yuheng AU - Di, Yan AU - Gao, Shan T2 - Proceedings of the Thirty-Fifth International Joint Conference on Artificial Intelligence, IJCAI-26 PB - International Joint Conferences on Artificial Intelligence Organization PY - 2026 SP - 1785 EP - 1793 DO - 10.24963/ijcai.2026/199 UR - https://doi.org/10.24963/ijcai.2026/199 ER -Copied
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