AsySplat: Efficient Asymmetric 3D Gaussian Splatting for Long-Sequence Scene Modeling

Hugging Face Daily Papers Papers
3d-gaussian-splatting novel-view-synthesis asymmetric-architecture efficient-computation parameter-efficiency long-sequence

Summary

AsySplat proposes an asymmetric architecture that decouples geometry and appearance modeling in 3D Gaussian Splatting, achieving high efficiency for long-sequence scene modeling with nearly 800x speedup over optimization-based methods.

Recent generalizable 3D Gaussian Splatting models have advanced long-sequence novel view synthesis (NVS), but at the cost of substantial redundant computation. We identify that the redundancy can be mitigated based on two observations: (i) high-precision geometry is not strictly required for high-quality NVS; (ii) appearance learning is generally easier than geometry recovery. Motivated by these insights, we propose an asymmetric architecture that decouples geometry and appearance modeling. The geometry branch processes coarse-grained tokens with most of the parameters for multi-view reconstruction, while the appearance branch operates on fine-grained tokens to capture details using significantly fewer parameters. The two branches interact through bilateral connections, enabling mutual guidance for their respective tasks. This task-aware asymmetry reduces the computational redundancy and allocates the computation more judiciously, thereby increasing parameter efficiency and enabling smaller models to achieve strong performance. On 32-view 960P inputs, our model matches optimization-based methods while delivering nearly 800x speedup, and surpasses the zero-shot performance of state-of-the-art generalizable models with markedly fewer parameters and reduced training/inference overhead, achieving an overall efficiency improvement.
Original Article
View Cached Full Text

Cached at: 07/20/26, 09:41 AM

Paper page - AsySplat: Efficient Asymmetric 3D Gaussian Splatting for Long-Sequence Scene Modeling

Source: https://huggingface.co/papers/2607.10995

Abstract

Recentgeneralizable3DGaussianSplattingmodelshaveadvancedlong-sequencenovelviewsynthesis(NVS),butatthecostofsubstantialredundantcomputation.Weidentifythattheredundancycanbemitigatedbasedontwoobservations:(i)high-precisiongeometryisnotstrictlyrequiredforhigh-qualityNVS;(ii)appearancelearningisgenerallyeasierthangeometryrecovery.Motivatedbytheseinsights,weproposeanasymmetricarchitecturethatdecouplesgeometryandappearancemodeling.Thegeometrybranchprocessescoarse-grainedtokenswithmostoftheparametersformulti-viewreconstruction,whiletheappearancebranchoperatesonfine-grainedtokenstocapturedetailsusingsignificantlyfewerparameters.Thetwobranchesinteractthroughbilateralconnections,enablingmutualguidancefortheirrespectivetasks.Thistask-awareasymmetryreducesthecomputationalredundancyandallocatesthecomputationmorejudiciously,therebyincreasingparameterefficiencyandenablingsmallermodelstoachievestrongperformance.On32-view960Pinputs,ourmodelmatchesoptimization-basedmethodswhiledeliveringnearly800xspeedup,andsurpassesthezero-shotperformanceofstate-of-the-artgeneralizablemodelswithmarkedlyfewerparametersandreducedtraining/inferenceoverhead,achievinganoverallefficiencyimprovement.

View arXiv pageView PDFProject pageGitHub7Add to collection

Models citing this paper0

No model linking this paper

Cite arxiv.org/abs/2607.10995 in a model README.md to link it from this page.

Datasets citing this paper0

No dataset linking this paper

Cite arxiv.org/abs/2607.10995 in a dataset README.md to link it from this page.

Spaces citing this paper0

No Space linking this paper

Cite arxiv.org/abs/2607.10995 in a Space README.md to link it from this page.

Collections including this paper0

No Collection including this paper

Add this paper to acollectionto link it from this page.

Similar Articles

GlobalSplat: Efficient Feed-Forward 3D Gaussian Splatting via Global Scene Tokens

Hugging Face Daily Papers

GlobalSplat introduces an efficient feed-forward framework for 3D Gaussian splatting that achieves compact and consistent scene reconstruction using global scene tokens, reducing computational overhead and inference time to under 78ms. The method uses a coarse-to-fine training approach to prevent representation bloat while maintaining competitive novel-view synthesis performance with significantly fewer Gaussians (16K) compared to dense baselines.

ZipSplat: Fewer Gaussians, Better Splats

Hugging Face Daily Papers

ZipSplat is a token-based feed-forward 3D Gaussian Splatting model that uses k-means clustering to decouple Gaussian placement from the pixel grid, achieving ~6x fewer Gaussians while setting new state-of-the-art results on DL3DV and RealEstate10K without requiring ground-truth poses or intrinsics.

Better Gaussian Splatting in Julia

Hacker News Top

GaussianSplatting.jl 2.0 release brings multi-GPU backend support via KernelAbstractions.jl, a multithreaded UI, MCMC densification strategy, and depth/geometry supervision for better 3D reconstruction.