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

3D Gaussian Splatting for Real-Time Radiance Field Rendering