The field of 3D rendering and reconstruction is moving towards more efficient and accurate methods for capturing and representing complex scenes. Recent developments have focused on improving the performance of bidirectional path tracing, stochastic rasterization, and 3D Gaussian splatting. These advancements have enabled faster rendering times, improved visual fidelity, and increased control over render cost and quality. Notably, the integration of physical priors and illumination-agnostic methods has shown promise in addressing challenging lighting conditions and improving novel view synthesis results. Additionally, the use of hierarchical attention networks, mesh compression, and implicit neural representations has demonstrated potential for efficient and high-quality 3D attribute compression and surface reconstruction. Noteworthy papers include: StochasticSplats, which combines 3D Gaussian splatting with stochastic rasterization for efficient and accurate rendering. LITA-GS, which introduces a novel illumination-agnostic novel view synthesis method via reference-free 3DGS and physical priors, achieving state-of-the-art results and faster inference speed.