【摘要】 针对现有基于三维表面重建的图像重照明方法存在纹理噪点、重照明质量不足及特征空间利用率低等问题，文章提出一种基于单张量辐射场的数字服装重照明方法。该方法首先利用球面高斯函数和多层感知机，分别模拟环境直射光和服装表面间的间接反射光，以构建一个精准的入射光场；接着通过引入梯度引导平滑策略，优化从特征空间中提取双向反射分布函数模型参数的过程。最后，利用简化的反射率方程，结合入射光场、双向反射分布模型及特征空间，成功地渲染出高质量的服装重照明图像。实验结果表明，该方法有效地减少了服装纹理噪点，显著降低了服装重照明的失真现象。相较于先进方法，该方法在生成服装新视角图像方面，各项评估指标的平均提升约9.922%;在服装重照明结果方面，各项评估指标的平均提升约4.549%。更多还原

【Abstract】 The technology of garment relighting carries substantial research significance in the domains of online garment sales, virtual fitting, and personalized customization. Moreover, relighting methods based on implicit 3D models have garnered considerable attention in the fields of computer vision and computer graphics. However, existing scene relighting techniques face inherent challenges when applied to garment datasets, such as texture noise and relighting distortion. To address these shortcomings, this paper proposes an innovative garment relighting method that operates in the feature tensor, to effectively mitigate these issues.The relighting process involves incorporating the relighting component into the tensorial radiance fields to jointly optimize the feature space. To simulate the direct ambient light and the indirect light reflected between garment surfaces, Spherical Gauss and MLP techniques are employed to construct an incident light field. Additionally, a gradient-guided smoothing strategy is utilized to optimize the extraction of parameters from the bidirectional reflectance distribution function model, which are derived from the feature tensor. Finally, the garment relighting image is rendered by combining the incident light field, the bidirectional reflection distribution model, and the feature tensor using the simplified reflectivity equation.This article presents experimental results on three garment datasets, comparing them with advanced methods such as Physg and InvRender. The results demonstrate that our method achieves an average improvement of about 4.549% in generating garment images from novel view and approximately 9.922% in generating garment images under relighting conditions, as evaluated using three indicators. The article visually demonstrates the effectiveness of our proposed method in reducing texture noise and reillumination distortion. Ablation experiments are also conducted, examining the impact of gradient-guided smoothing strategies and the use of single or multiple addition feature tensor for garment feature storage. The article shows that using multiple addition feature tensor does not enhance the quality of garment image generation but increases the training time. By comparison, using addition single feature tensor achieves a minimal improvement of only 0.327% but significantly increases the training time. Our method, which employs a single feature tensor, significantly shortens training time and improves the quality of garment relighting images by approximately 8.870% compared to the other models. The article compares the experimental results of different lighting strategies(DL, DL+SGID, DL+MLPID, DL+SGID+Vis, and DL+MLPID+Vis) in garment image rendering. The results indicate that the combination of indirect light, direct light, and visibility achieves the best generation outcomes. The article compares experimental results obtained by employing different gradient descent strategies(GD, GD+RSL, and GD+GGS) in garment image rendering. The results indicate that our proposed gradient-guided smoothing strategy enhances the quality novel view garment images by approximately 4.583% and relighting garment images by about 6.096% compared to traditional relative smoothing loss methods.This paper introduces a garment relighting model based on Tensorf for 3D garment relighting. The relighting module is integrated into Tensorf, which encompasses the incident light field combined with indirect light, the BRDF model based on the Disney principle, and the simplified reflectance equation. As a result, garment relighting from any perspective is achieved. The experimental results demonstrate that the proposed garment relighting model effectively leverages the feature tensor from Tensorf. The introduction of gradient smoothing loss contributes to the improved accuracy of BRDF parameters, reduces texture noise commonly encountered in existing methods applied to garment datasets, and enhances the accuracy of relighting. The evaluation indexes indicate that the model produces superior results compared to existing advanced methods for generating novel view images and relighting outcomes. However, it is worth noting that the surface reconstruction in this method relies on the tensor radiation field, which may result in rendering points with low effectiveness, leading to errors in the reconstruction of hollow areas on the surface. Future work will address this issue accordingly.更多还原