BudgetSplat: A Feed-Forward 3DGS Method That Limits Gaussian Count to a Specified Ratio, Published on arXiv

What happened

The paper "BudgetSplat: Budget-Controllable Generalizable Gaussian Splatting" was submitted to arXiv with a date of November 4, 2026. The five authors are Yuxi Hu, Jun Zhang, Wei Mao, Yuchen Rao and Friedrich Fraundorfer.

The target is the feed-forward approach, which builds 3D Gaussian Splatting (3DGS) from a few images in a single inference pass. The authors point out that existing methods that place one Gaussian per pixel tend to be redundant, and that representation size and rendering cost rise sharply as resolution or the number of input views increases.

The proposed method adjusts the final Gaussians to a retention ratio specified by the user. The authors say they formulate this not as simple pruning by importance ranking, but as a problem that jointly considers importance, spatial coverage and redundancy across views.

How it works

Intra-Cross Mask Supervision is a supervision signal that accounts for structure, coverage and redundancy, and is used to train the model to learn the importance of each Gaussian.

Budget-conditioned feature modulation adjusts the opacity and attributes of the Gaussians according to the requested retention ratio.

Coverage-Aware Budget Selection combines patch-level pre-selection, view-level opacity ranking and 3D redundancy suppression. The authors say this produces a compact representation with less spatial bias.

A single model handles different numbers of input views and multiple test-time budgets. Retraining for each budget and per-scene compression are reportedly not required.

By the numbers

Why it matters

When 3DGS is used on the web or on mobile devices, data size and rendering load are constraints. If the size can be specified at generation time, it could be used to serve different outputs depending on the capability of the target device.

If it avoids per-scene optimization and post-hoc compression, it could be an option for quickly building lightweight scenes from a small number of images. However, its practical effect will have to wait for released code and additional verification.

Caveats and open questions

• The only source material is the arXiv abstract. Specific compression ratios, figures such as PSNR, comparison baselines and processing times are not confirmed.
• Whether code or trained models are released is not confirmed in the source material.
• The evaluation is described as indoor reconstruction and cross-dataset generalization, so behavior in outdoor or large-scale scenes is unconfirmed.
• The claim that quality is maintained comes from the authors, and the source material contains no third-party verification.

Glossary

Feed-forward 3DGS

An approach that outputs 3DGS in a single inference pass of a trained network, without per-scene optimization.

Pixel-aligned

A design that generates one Gaussian for each pixel of the input image. It is said to tend toward redundancy.

Retention ratio

The proportion of generated Gaussians that remain in the final representation. In this paper, it corresponds to the budget.

Sources

Primary sources (publisher, paper, repository)

• BudgetSplat: Budget-Controllable Generalizable Gaussian SplattingarXiv（arxiv.org）・en

The X post that surfaced this story

• View @CSVisionPapers's post on Xx.com

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