PhysIR-Splat: Physically Consistent Thermal Infrared Radiative Transfer in 3D Gaussian Splatting
Jingyuan Gao, Yumeng Hu, Fei Gao, Mingjin Zhang
Abstract
Thermal infrared (TIR) 3D reconstruction provides geometry that is intrinsically coupled to the temperature field, even in low-light, nighttime, and smoke-obscured environments. TIR imaging measures self-emitted thermal radiation driven by object temperature and is largely independent of external illumination; therefore, simply carrying over visible-spectrum assumptions to TIR-based 3D reconstruction and novel view synthesis (NVS) often results in floating artifacts and blurred edges. In addition, radiometric inconsistency and low contrast in TIR weaken structurefrom-motion (SfM) initialization, which in turn hinders subsequent 3D Gaussian Splatting (3DGS) optimization. We present PhysIR-Splat, a 3DGS framework that follows infrared radiative transfer: we explicitly model temperature, emissivity, and environmental irradiance on Gaussian primitives and, during rendering, jointly account for thermal emission, the reflected component, and atmospheric transmittance to produce physically consistent thermal synthesis. We also introduce VGGT-IR, a Transformer-based feed-forward initializer that takes TIR input with optional RGB and directly regresses camera poses and initial geometry, providing a modality-aligned and stable starting point for PhysIR-Splat. Extensive experiments demonstrate that our method significantly surpasses existing approaches in thermal reconstruction quality and cross-view consistency, effectively suppressing floating artifacts and enhancing boundary sharpness. The source code will be publicly available at https:// github.com/JingyuanGao0919/physir- splat.
Ask about this paper
Your agent reads all of it.
Lune indexed this paper to the last equation, along with the top-tier papers that cite it. Ask a question and the answer quotes them.
Your agent calls
Luneget_paper_fulltext
Free to start. No credit card required.
Terminal
Install the CLIlune papers fulltext 394539a2-dc2b-400b-8ed8-8d1150a3322eBuilds on9
- 3D Gaussian Splatting for Real-Time Radiance Field RenderingBernhard Kerbl, Georgios Kopanas, Thomas Leimkühler, George DrettakisSIGGRAPH 2023 · 5,687 citations
- Instant neural graphics primitives with a multiresolution hash encodingThomas Müller, Alex Evans, Christoph Schied, Alexander KellerSIGGRAPH 2022 · 4,089 citations
- Vision Transformers for Dense PredictionRené Ranftl, Alexey Bochkovskiy, Vladlen KoltunICCV 2021 · 2,647 citations
- NeuS: Learning Neural Implicit Surfaces by Volume Rendering for Multi-view ReconstructionPeng Wang, Lingjie Liu, Yuan Liu, Christian Theobalt et al.NeurIPS 2021 · 2,500 citations
- ScanNet++: A High-Fidelity Dataset of 3D Indoor ScenesChandan Yeshwanth, Yueh-Cheng Liu, Matthias Nießner, Angela DaiICCV 2023 · 659 citations
Related papers
- GS-IR: 3D Gaussian Splatting for Inverse RenderingZhihao Liang, Qi Zhang, Ying Feng, Ying Shan et al.CVPR 2024
- Thermal is Always Wild: Characterizing and Addressing Challenges in Thermal-Only Novel View SynthesisM. Kerem Aydin, Vishwanath Saragadam, Emma AlexanderCVPR 2026
- Shape-Shifting Splats: Realtime Context Translation for Gaussian Splatting in VRThomas Kernbauer, Simon Fussi, Philipp Fleck, Clemens ArthIEEE VR 2026
- NTR-Gaussian: Nighttime Dynamic Thermal Reconstruction with 4D Gaussian Splatting Based on ThermodynamicsKun Yang, Yuxiang Liu, Zeyu Cui, Yu Liu et al.CVPR 2025
- RTR-GS: 3D Gaussian Splatting for Inverse Rendering with Radiance Transfer and ReflectionYongyang Zhou, Fanglue Zhang, Zichen Wang, Lei ZhangACM MM 2025 · 4 citations
