看见热量：从光学图像合成高分辨率木材热响应
Seeing the Heat: Synthesizing High-Resolution Wood Thermal Responses from Optical Imagery
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中文总结 AI 辅助
提出端到端框架，利用木材RGB图像与热响应间的物理联系，通过有限元数据引擎合成高分辨率热数据，并训练基于DINOv3的神经代理模型，实现低成本高分辨率热推断。
中文摘要 AI 辅助
木材的热行为是先进材料组装中的一个关键因素。然而，像素级热分析从根本上受到红外热成像固有的低分辨率和噪声的限制。为了解决这一问题，我们引入了一个端到端的计算框架，直接从木材RGB图像合成高分辨率热响应。我们首先建立了一个核心的物理联系：由于天然木材中空间颜色变化由细胞解剖结构驱动，光学强度可作为局部固体体积分数的可靠几何代理。利用这一理论见解，我们开发了一个自动化的有限元方法数据引擎，将像素级光学强度映射到三维热力学体素网格，生成高保真合成热响应。我们发现：1）当沿厚度方向的热导率均匀或线性时，木材RGB图像与其对应的热响应表现出极端的形态相似性，且横向热扩散作为低通滤波器平滑了高频细节；2）当沿厚度方向的热导率随机时，这种形态相似性被破坏，木材的三维结构主导其热响应。我们进一步利用这些合成热响应来监督一个基于DINOv3基础模型构建的神经代理模型。我们的结果表明，该神经代理模型成功内化了控制热力学定律，从而绕过了计算昂贵的模拟，实现了高分辨率热推断。该方法有效桥接了语义域和热力学域，开启了对细粒度木材热响应的系统性、像素级分析。项目：此https URL
英文摘要
The thermal behavior of wood is a critical factor in advanced material assembly. However, pixel-level thermal analysis remains fundamentally constrained by the low resolution and noise inherent to infrared thermography. To address this, we introduce an end-to-end computational framework that synthesizes high-resolution thermal responses directly from wood RGB images. We first establish a core physical linkage: because spatial color variation in natural wood is driven by cellular anatomy, optical intensity serves as a reliable geometric proxy for the localized solid volume fraction. By leveraging this theoretical insight, we develop an automated finite-element-method data engine that maps pixel-level optical intensity to a 3D thermodynamic voxel grid, generating high-fidelity synthetic thermal responses. We find that 1) when the thermal conductivity along the thickness direction is uniform or linear, wood RGB images and their corresponding thermal responses exhibit extreme morphological similarities, and the lateral thermal diffusion acts as a low-pass filter that smooths out high-frequency details; 2) when the thermal conductivity along the thickness direction is random, such morphological similarities are destroyed, and wood's 3D structure dominantly governs its thermal response. We further utilize these synthetic thermal responses to supervise a neural surrogate model built upon the DINOv3 foundation model. Our results demonstrate that the neural surrogate model successfully internalizes the governing thermodynamic laws, thereby bypassing computationally expensive simulations and enabling high-resolution thermal inference. This methodology effectively bridges the semantic and thermodynamic domains, unlocking systematic, pixel-level analysis of fine-grained wood thermal responses. Project: https://zekifayes.github.io/seeheat
