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This paper provides a rigorous mathematical analysis of 3D Gaussian Splatting, establishing conditions under which the method recovers true scene geometry versus overfitting to view-dependent artifacts. By isolating geometry from appearance through a first-hit rendering abstraction, the authors prove that bounded angular capacity naturally favors surface-consistent solutions while unrestricted models can degenerate into incorrect opaque billboards. The findings offer critical theoretical clarity for advancing neural rendering techniques and ensuring geometric fidelity in real-world reconstruction tasks.
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When 3D Gaussian Splatting Recovers Real Surfaces

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arXiv:2608.30054v1 Announce Type: new Abstract: When does 3D Gaussian Splatting (3DGS) recover the true scene surface rather than just overfitting view-dependent appearance? We answer this by developing a mathematical framework based on a first-hit rendering abstraction that cleanly isolates geometry from appearance. We prove that geometric misalignment forcefully converts spatial textures into high-frequency angular signals via parallax. This establishes a strict identifiability window: if angular capacity is bounded, surface-consistent solutions are mathematically preferred; if unrestricted, the same images can be perfectly explained by an incorrect, opaque billboard geometry. Experiments on synthetic stress tests confirm this prediction, showing billboard failures emerge precisely at high angular capacities. Conversely, in the real-world datasets we evaluate under standard capture protocols, reconstructions remain surface-consistent even at high SH degrees, which is consistent with the prediction that rich spatial texture can push billboard solutions outside the tested angular-capacity range.

Abstract:When does 3D Gaussian Splatting (3DGS) recover the true scene surface rather than just overfitting view-dependent appearance? We answer this by developing a mathematical framework based on a first-hit rendering abstraction that cleanly isolates geometry from appearance. We prove that geometric misalignment forcefully converts spatial textures into high-frequency angular signals via parallax. This establishes a strict identifiability window: if angular capacity is bounded, surface-consistent solutions are mathematically preferred; if unrestricted, the same images can be perfectly explained by an incorrect, opaque billboard geometry. Experiments on synthetic stress tests confirm this prediction, showing billboard failures emerge precisely at high angular capacities. Conversely, in the real-world datasets we evaluate under standard capture protocols, reconstructions remain surface-consistent even at high SH degrees, which is consistent with the prediction that rich spatial texture can push billboard solutions outside the tested angular-capacity range.

Accepted at ECCV 2026. 30 pages, 3 figures, 1 table; includes supplementary material

Machine Learning (cs.LG); Computer Vision and Pattern Recognition (cs.CV)

arXiv:2608.30054 [cs.LG]

(or arXiv:2608.30054v1 [cs.LG] for this version)

https://doi.org/10.48550/arXiv.2608.30054 Focus to learn more arXiv-issued DOI via DataCite (pending registration)

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