We proposed Precomputed RadianceTransfer of GaussianSplats (PRTGS), a real-time high-quality relighting method for Gaussian splats in low-frequency lighting environments that captures soft shadows and interreflections by precomputing 3D Gaussian splats' radiance transfer. Existing studies have demonstrated that 3D Gaussian splatting (3DGS) outperforms neural fields' efficiency for dynamic lighting scenarios. However, the current relighting method based on 3DGS still struggles to compute high-quality shadow and indirect illumination in real time for dynamic light, leading to unrealistic rendering results. We solve this problem by precomputing the expensive transport simulations required for complex transfer functions like shadowing, the resulting transfer functions are represented as dense sets of vectors or matrices for every Gaussian splat. We introduce distinct precomputing methods tailored for training and rendering stages, along with unique ray tracing and indirect lighting precomputation techniques for 3D Gaussian splats to accelerate training speed and compute accurate indirect lighting related to environment light. Experimental analyses demonstrate that our approach achieves state-of-the-art visual quality while maintaining competitive training times and allows high-quality real-time (30+ fps) relighting for dynamic light and relatively complex scenes at 1080p resolution.
我们提出了预计算高斯点光源辐射传输（PRTGS），这是一种针对低频照明环境中高斯点光源的实时高质量重新照明方法，能够通过预计算 3D 高斯点光源的辐射传输来捕捉柔和阴影和间接反射。现有研究已证明 3D 高斯点光源（3DGS）在动态照明场景中比神经场更高效。然而，当前基于 3DGS 的重新照明方法在实时计算高质量阴影和间接光照时仍然存在困难，导致不真实的渲染结果。我们通过预计算复杂传输函数（如阴影）所需的昂贵传输模拟来解决这个问题，得到的传输函数以密集的向量或矩阵集表示每个高斯点光源。我们引入了针对训练和渲染阶段的不同预计算方法，以及独特的光线追踪和间接光照预计算技术，以加速训练速度并计算与环境光相关的准确间接光照。实验分析表明，我们的方法在保持竞争的训练时间的同时实现了最先进的视觉质量，并允许在 1080p 分辨率下对动态光源和相对复杂场景进行高质量的实时（30+ fps）重新照明。