It is proved that geometric misalignment forcefully converts spatial textures into high-frequency angular signals via parallax, which 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.
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.
Across datasets, major structures were generally reproduced well, whereas sparse foliage, low-contrast repetitive textures, fine surface details, and distant background objects required ROI-level verification, whereas sparse foliage, low-contrast repetitive textures, fine surface details, and distant background objects...
Tomohiro Mizoguchi· Italian National Conference...· 0 citations
We present VolS-GS, a relightable Gaussian splatting framework that reconstructs objects from one-light-at-a-time (OLAT) captures and renders them under novel lighting and viewpoints. Relightable Gaussian Splatting methods typically model appearance independently at each primitive, which makes non-local effects difficu...
The proposed framework provides a simple and effective path toward fast and inherently compact 3DGS training, and leverages a synergistic design: an L2 reconstruction loss to provide error-proportional gradients that stabilize optimization, and a novel Polarized Opacity Prior to actively manage the Gaussian population.
Zi-Ming Wang, Kai-Wen Duan, Ko-Wei Huang et al.· 0 citations
Standard 3D Gaussian Splatting (3DGS) learns geometry and appearance jointly from RGB supervision, making it difficult to isolate how luminance and chroma contribute to the learned representation. We study this by training models under different channel supervision, freezing their non-appearance parameters (position, s...
View-Structured Conformal Prediction (VSCP) is proposed, which splits the pre-calibration scale into a spatial shape from the renderer and a transferable view-difficulty factor, which predicts the smallest view-wise multiplier that shape needs.
Jun-Zheng Chu, Bin Pan, Zhen-Wei Shi· 0 citations
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