Radiance Field methods have recently revolutionized novel-view synthesis of scenes captured with multiple photos or videos. However, achieving high visual quality still requires neural networks that are costly to train and render, while recent faster methods inevitably trade off speed for quality. For unbounded and complete scenes (rather than isolated objects) and 1080p resolution rendering, no current method can achieve real-time display rates. We introduce three key elements that allow us to achieve state-of-the-art visual quality while maintaining competitive training times and importantly allow high-quality real-time (>= 30 fps) novel-view synthesis at 1080p resolution. First, starting from sparse points produced during camera calibration, we represent the scene with 3D Gaussians that preserve desirable properties of continuous volumetric radiance fields for scene optimization while avoiding unnecessary computation in empty space; Second, we perform interleaved optimization/density control of the 3D Gaussians, notably optimizing anisotropic covariance to achieve an accurate representation of the scene; Third, we develop a fast visibility-aware rendering algorithm that supports anisotropic splatting and both accelerates training and allows realtime rendering. We demonstrate state-of-the-art visual quality and real-time rendering on several established datasets.

翻译：辐射场方法近期彻底革新了多张照片或视频捕捉场景的新视角合成技术。然而，要获得高视觉质量仍需依赖训练和渲染成本高昂的神经网络，而近期更快速的方法不可避免地牺牲质量以换取速度。针对无界完整场景（而非孤立物体）及1080p分辨率渲染，现有方法均无法实现实时显示帧率。我们引入三个关键元素，在保持有竞争力的训练时间的同时实现了顶尖视觉质量，更重要的是能够以1080p分辨率高质量实时（≥30帧/秒）新视角合成。首先，从相机标定产生的稀疏点出发，我们用3D高斯函数表示场景，既保留了连续体积辐射场用于场景优化的理想特性，又避免了在空区域的不必要计算；其次，我们对3D高斯执行交错优化/密度控制，特别优化各向异性协方差以实现场景精准表示；最后，我们开发了支持各向异性溅射的快速可见性感知渲染算法，既能加速训练又能实现实时渲染。我们在多个已建立数据集上展示了顶尖视觉质量与实时渲染性能。