3D Gaussian Splatting for Real-Time Radiance Field Rendering
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.
64d324d461b976fdb06eec74
Bernhard Kerbl
admin_assigned
2024-02-09T13:54:23.154Z
645578a0273f649830286cc9
/avatars/72418cc69eab830abab2bd8fd8a397c7.svg
Bernhard Kerbl
user
SnoSixtyTwo
64d324d461b976fdb06eec75
Georgios Kopanas
64d324d461b976fdb06eec76
Thomas Leimkühler
64d324d461b976fdb06eec77
George Drettakis
claimed_verified
2024-09-18T11:34:53.099Z
66eaa4ba1654494d77f28d42
/avatars/42e6d78bc6c32ea43dad0684b210c815.svg
George Drettakis
user
gdrett
3D Gaussian Splatting for Real-Time Radiance Field Rendering
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.
A method using 3D Gaussians for scene representation and optimized rendering allows high-quality, real-time novel-view synthesis at 1080p resolution.
radiance field novel-view synthesis 3D Gaussians volumetric radiance fields anisotropic covariance visibility-aware rendering anisotropic splatting