| 胡明.融合Transformer和卷积神经网络的全局光照估计[J].安徽建筑大学学报,2026,34(3):57-65 |
| 融合Transformer和卷积神经网络的全局光照估计 |
| Globally lighting Estimation by Integrating Transformer and Convolutional Neural Network |
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| DOI： |
| 中文关键词: AR 光照估计 球面谐波系数 transformer 卷积神经网络 |
| 英文关键词: augmented reality（AR） illumination estimation Spherical Harmonic（SH）coefficient transformer convolutional neuralnetworks |
| 基金项目:安徽省中青年教师培养行动项目（JNFX2023116）；安徽省高校自然科学研究重点项目（2024AH052024） |
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| 中文摘要: |
| 增强现实（AR）技术的目标是实现虚拟物体与真实世界的视觉一致性。然而，受相机视场范围限制，加之真实场景光照条件复杂，开发者在还原全景光照时，难以精准估测低频光照信息。针对该问题，本文提出一种融合Transformer与深度可分离卷积神经网络（CNN）的光照估计模型EViTLight。该模型可高效提取场景低频光照特征，仅依靠单张有限视场（LFOV）低动态范围（LDR）图像，就能估算表征漫反射的球面谐波（SH）系数；再通过构建SH 函数，近似还原场景低频光照。在公开数据集上开展对比实验，结果显示：相较于四种经典光照估计算法，EViTLight模型综合性能更优，均方根误差（RMSE）达0.016 5，结构相异性（DSSIM）为0.011 9。定量对比实验与原型系统开发相结合，进一步验证了该模型的有效性与实用性。 |
| 英文摘要: |
| The goal of Augmented Reality（AR）technology is to achieve visual consistency between virtual objects and the real world. However，due to the limitations of the camera's field of view and the complexity of lighting conditions in real scenes，developers often face challenges in accurately estimating low-frequency lighting information when recovering panoramic lighting. To address this issue， we propose a lighting estimation model，EViTLight，based on Transformer and Depthwise Separable Convolutional Neural Networks （CNN）. The model effectively extracts low-frequency lighting features from the scene and estimates Spherical Harmonics（SH）coefficients， representing diffuse reflection，from a single Low Dynamic Range（LDR）image with a Limited Field of View（LFOV）. The SH coefficients are then used to approximate low-frequency lighting in the scene through the construction of SH functions. Experimental results on public datasets show that，compared with four classical lighting estimation methods，the EViTLight model achieves superior performance， with a Root Mean Square Error（RMSE）of 0.016 5 and a Structural Dissimilarity（DSSIM）of 0.011 9. Furthermore，both quantitative experiments and the development of a prototype system validate the effectiveness and practicality of the proposed model. |
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