Kavli Affiliate: Michael Wimmer
| First 5 Authors: Adam Celarek, George Kopanas, George Drettakis, Michael Wimmer, Bernhard Kerbl
| Summary:
Since its introduction, 3D Gaussian Splatting (3DGS) has become an important
reference method for learning 3D representations of a captured scene, allowing
real-time novel-view synthesis with high visual quality and fast training
times. Neural Radiance Fields (NeRFs), which preceded 3DGS, are based on a
principled ray-marching approach for volumetric rendering. In contrast, while
sharing a similar image formation model with NeRF, 3DGS uses a hybrid rendering
solution that builds on the strengths of volume rendering and primitive
rasterization. A crucial benefit of 3DGS is its performance, achieved through a
set of approximations, in many cases with respect to volumetric rendering
theory. A naturally arising question is whether replacing these approximations
with more principled volumetric rendering solutions can improve the quality of
3DGS. In this paper, we present an in-depth analysis of the various
approximations and assumptions used by the original 3DGS solution. We
demonstrate that, while more accurate volumetric rendering can help for low
numbers of primitives, the power of efficient optimization and the large number
of Gaussians allows 3DGS to outperform volumetric rendering despite its
approximations.
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