Thermoxels: a voxel-based method to generate simulation-ready 3D thermal models

2025-04-06

AI Summary

Current building energy efficiency assessment is hindered by qualitative thermography and finite element analysis (FEA) reliant on manual CAD modeling, impeding data-driven automation. While neural radiance fields (NeRF) and related 3D reconstruction methods generate geometry from sparse RGB images, their outputs lack explicit volumetric segmentation and thermal properties—rendering them unsuitable for direct FEA integration. This paper introduces the first end-to-end thermal-geometric joint reconstruction framework: leveraging differentiable voxel rendering, it jointly fuses sparse RGB and thermal imagery to implicitly encode a continuous temperature field. We propose a novel thermal-aware voxel representation and enable fully automatic conversion from voxels to watertight surfaces and, ultimately, FEA-ready tetrahedral meshes. The method integrates mainstream geometric priors, produces high-fidelity, thermally consistent meshes across diverse scenarios, ensures stable convergence in thermal conduction simulation, achieves state-of-the-art image synthesis quality, and substantially reduces dependence on manual CAD modeling.

Technology Category

• Knowledge Representation and Reasoning: Geometric, Spatial, and Temporal Reasoning
• Computer Vision: Remote Sensing / Geospatial AI
• Intelligent Robots: Multimodal Perception & Sensor Fusion

Application Category

• Search and Retrieval-Augmented AI: Retrieval-Augmented Generation (RAG) and multi-modal RAG
• Semantics and Knowledge: Methods to enhance, augment, integrate or synergize semantic models such as knowledge graphs and LLMs
• Systems and Infrastructure for Web, Mobile and WoT: Sustainability and carbon-aware systems for Web, mobile, and WoT

Abstract

In the European Union, buildings account for 42% of energy use and 35% of greenhouse gas emissions. Since most existing buildings will still be in use by 2050, retrofitting is crucial for emissions reduction. However, current building assessment methods rely mainly on qualitative thermal imaging, which limits data-driven decisions for energy savings. On the other hand, quantitative assessments using finite element analysis (FEA) offer precise insights but require manual CAD design, which is tedious and error-prone. Recent advances in 3D reconstruction, such as Neural Radiance Fields (NeRF) and Gaussian Splatting, enable precise 3D modeling from sparse images but lack clearly defined volumes and the interfaces between them needed for FEA. We propose Thermoxels, a novel voxel-based method able to generate FEA-compatible models, including both geometry and temperature, from a sparse set of RGB and thermal images. Using pairs of RGB and thermal images as input, Thermoxels represents a scene's geometry as a set of voxels comprising color and temperature information. After optimization, a simple process is used to transform Thermoxels' models into tetrahedral meshes compatible with FEA. We demonstrate Thermoxels' capability to generate RGB+Thermal meshes of 3D scenes, surpassing other state-of-the-art methods. To showcase the practical applications of Thermoxels' models, we conduct a simple heat conduction simulation using FEA, achieving convergence from an initial state defined by Thermoxels' thermal reconstruction. Additionally, we compare Thermoxels' image synthesis abilities with current state-of-the-art methods, showing competitive results, and discuss the limitations of existing metrics in assessing mesh quality.

Problem

• Generates simulation-ready 3D thermal models from sparse images
• Addresses limitations of qualitative thermal imaging for energy savings
• Automates FEA-compatible modeling to replace manual CAD design

Innovation

• Voxel-based method for thermal modeling
• Generates FEA-compatible models from images
• Converts voxels to tetrahedral meshes