GS^3：三重高斯飞溅的高效重照明

摘要

我们提出了基于空间和角度高斯表示以及三重分层过程的方法，用于从多视点照明输入图像实时、高质量地合成新颖的光照和视图。为了描述复杂外观，我们采用朗伯加上混合角度高斯作为每个空间高斯的有效反射函数。为了生成自阴影，我们将所有空间高斯向光源投射以获得阴影值，然后通过一个小型多层感知器进一步细化。为了补偿其他效果，如全局光照，另一个网络被训练用于计算并添加每个空间高斯的RGB元组。我们的表示方法在30个样本上展示了其有效性，这些样本在几何形状（从实心到蓬松）和外观（从半透明到各向异性）上变化很大，并使用不同形式的输入数据，包括合成/重建对象的渲染图像、手持相机和闪光灯拍摄的照片，或来自专业光场的图像。我们在单个普通GPU上实现了40-70分钟的训练时间和90 fps的渲染速度。我们的结果在质量/性能方面与最先进的技术相比具有竞争力。我们的代码和数据可在https://GSrelight.github.io/ 上公开获取。

English

We present a spatial and angular Gaussian based representation and a triple splatting process, for real-time, high-quality novel lighting-and-view synthesis from multi-view point-lit input images. To describe complex appearance, we employ a Lambertian plus a mixture of angular Gaussians as an effective reflectance function for each spatial Gaussian. To generate self-shadow, we splat all spatial Gaussians towards the light source to obtain shadow values, which are further refined by a small multi-layer perceptron. To compensate for other effects like global illumination, another network is trained to compute and add a per-spatial-Gaussian RGB tuple. The effectiveness of our representation is demonstrated on 30 samples with a wide variation in geometry (from solid to fluffy) and appearance (from translucent to anisotropic), as well as using different forms of input data, including rendered images of synthetic/reconstructed objects, photographs captured with a handheld camera and a flash, or from a professional lightstage. We achieve a training time of 40-70 minutes and a rendering speed of 90 fps on a single commodity GPU. Our results compare favorably with state-of-the-art techniques in terms of quality/performance. Our code and data are publicly available at https://GSrelight.github.io/.

GS^3：三重高斯飞溅的高效重照明

GS^3: Efficient Relighting with Triple Gaussian Splatting

摘要

Summary

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