We present PRTGaussian, a realtime relightable novel-view synthesis method made possible by combining 3D Gaussians and Precomputed Radiance Transfer (PRT). By fitting relightable Gaussians to multi-view OLAT data, our method enables real-time, free-viewpoint relighting. By estimating the radiance transfer based on high-order spherical harmonics, we achieve a balance between capturing detailed relighting effects and maintaining computational efficiency. We utilize a two-stage process: in the first stage, we reconstruct a coarse geometry of the object from multi-view images. In the second stage, we initialize 3D Gaussians with the obtained point cloud, then simultaneously refine the coarse geometry and learn the light transport for each Gaussian. Extensive experiments on synthetic datasets show that our approach can achieve fast and high-quality relighting for general objects.
我们提出了PRTGaussian，这是一种结合了3D高斯点绘和预计算辐射传输（Precomputed Radiance Transfer，PRT）的实时可重光照新视角合成方法。通过将可重光照的高斯模型拟合到多视角的单光源照明测试（OLAT）数据中，我们的方法实现了实时、自由视角的重光照。通过基于高阶球谐函数估计辐射传输，我们在捕捉详细的重光照效果和保持计算效率之间取得了平衡。我们的方法采用了两阶段的处理过程：在第一阶段，我们从多视角图像中重建物体的粗略几何结构。在第二阶段，我们利用获得的点云初始化3D高斯模型，然后同时优化粗略几何结构并学习每个高斯点的光传输。大量的合成数据集实验表明，我们的方法能够快速、高质量地对一般物体进行重光照处理。