Dynamic Thermal Gaussians: Multimodal 4D Gaussian Splatting

2026-09-21

AI Summary

该研究提出了一种动态RGB-热成像重建框架，通过多模态动态场景表示和自适应生成特定模态高斯分布的方法解决了温度分布随时间变化的问题。

Abstract

Thermography plays a vital role in military and broader thermal analysis applications. Recent progress in 3D thermal reconstruction has extended temperature analysis from 2D to 3D space, yet most existing works assume static temperature distributions, neglecting the temporal dynamics of heat transfer in real-world environments. To address this limitation, we propose the first dynamic RGB-Thermal reconstruction framework for complex scenes. Our method jointly models RGB appearance, thermal observations, and scene geometry as they change over time. Specifically, we introduce a multimodal dynamic scene representation that anchors both the color and thermal modalities to a shared geometric substrate, ensuring their consistency under spatiotemporal deformations. We further design multimodal embeddings to enhance the motion expressiveness for each modality, and propose a multimodal routing mechanism that retains a unified set of shared multimodal Gaussians as the geometric backbone while adaptively spawning modality-specific Gaussians to strengthen the representational capacity in detail-rich regions of each individual modality. In addition, we contribute a novel benchmark dataset featuring high-frequency temperature variations to facilitate the evaluation of 4D reconstruction. Extensive experiments demonstrate that our method achieves high-fidelity spatiotemporal reconstruction of both appearance and temperature. Our code and dataset are available at: https://github.com/LinLif1869/DTG.

Problem

• Thermography
• Dynamic Thermal Reconstruction
• Spatiotemporal Dynamics
• Multimodal Scene Representation
• 4D Reconstruction

Innovation

• Dynamic RGB-Thermal Reconstruction
• Multimodal Scene Representation
• Multimodal Embeddings
• Adaptive Gaussian Spawning