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

• URL: http://arxiv.org/abs/2607.10995v1
• Date: Mon, 13 Jul 2026 01:39:19 GMT
• Title: AsySplat: Efficient Asymmetric 3D Gaussian Splatting for Long-Sequence Scene Modeling
• Abstract summary: Generalizable 3D Gaussian Splatting models have advanced long-sequence novel view synthesis (NVS)<n>We propose an asymmetric architecture that decouples geometry and appearance modeling.<n>On 32-view 960P inputs, our model matches optimization-based methods while delivering nearly 800x speedup.
• Score: 36.45427515934316
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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.

Related papers

• Compact Neural Appearance Models for Efficient Gaussian Splatting [1.579851447355843]
  primitive-based radiance fields such as 3D Gaussian Splatting typically model view-dependent appearance using low-order spherical harmonics (SH)<n>We present a thorough, end-to-end comparison of SH and recent spherical appearance models.<n>We introduce an implicit alternative that decodes compact per-primitive latent codes using a tiny shared pipeline.
  arXiv  Detail & Related papers  (2026-09-04T15:18:54Z)
• PointSplat: Efficient Geometry-Driven Pruning and Transformer Refinement for 3D Gaussian Splatting [2.5823344978857388]
  3D Gaussian Splatting has recently unlocked real-time, high-fidelity novel view synthesis by representing scenes using explicit 3D primitives.<n>Traditional methods often require millions of Gaussians to capture complex scenes, leading to significant memory and storage demands.<n>Recent approaches have addressed this issue through pruning and per-scene fine-tuning of Gaussian parameters, thereby reducing the model size while maintaining visual quality.
  arXiv  Detail & Related papers  (2026-04-10T20:56:18Z)
• LSRM: High-Fidelity Object-Centric Reconstruction via Scaled Context Windows [10.300202521638274]
  We introduce the Large Sparse Reconstruction Model to study how scaling transformer context windows impacts feed-forward 3D reconstruction.<n>We show that expanding the context window -- by substantially increasing the number of active object and image tokens -- remarkably narrows this gap and enables high-fidelity 3D object reconstruction and inverse rendering.
  arXiv  Detail & Related papers  (2026-04-06T21:21:12Z)
• NoiseFormer -- Noise Diffused Symmetric Attention Transformer [0.0]
  We propose a novel unified model architecture called Noise Diffused Symmetric Attention Transformer to enhance the model's performance.<n>The proposed model is validated upon GPT2 base model and the results reflect the performance gains falling between plain Symmetric attention and GPT2 base model.
  arXiv  Detail & Related papers  (2026-01-10T14:10:48Z)
• Lighter-X: An Efficient and Plug-and-play Strategy for Graph-based Recommendation through Decoupled Propagation [49.865020394064096]
  We propose textbfLighter-X, an efficient and modular framework that can be seamlessly integrated with existing GNN-based recommender architectures.<n>Our approach substantially reduces both parameter size and computational complexity while preserving the theoretical guarantees and empirical performance of the base models.<n>Experiments demonstrate that Lighter-X achieves comparable performance to baseline models with significantly fewer parameters.
  arXiv  Detail & Related papers  (2025-10-11T08:33:08Z)
• Geometric Algebra Planes: Convex Implicit Neural Volumes [70.12234371845445]
  We show that GA-Planes is equivalent to a sparse low-rank factor plus low-resolution matrix. We also show that GA-Planes can be adapted for many existing representations.
  arXiv  Detail & Related papers  (2024-11-20T18:21:58Z)
• GPS-Gaussian+: Generalizable Pixel-wise 3D Gaussian Splatting for Real-Time Human-Scene Rendering from Sparse Views [67.34073368933814]
  We propose a generalizable Gaussian Splatting approach for high-resolution image rendering under a sparse-view camera setting. We train our Gaussian parameter regression module on human-only data or human-scene data, jointly with a depth estimation module to lift 2D parameter maps to 3D space. Experiments on several datasets demonstrate that our method outperforms state-of-the-art methods while achieving an exceeding rendering speed.
  arXiv  Detail & Related papers  (2024-11-18T08:18:44Z)
• MVSGaussian: Fast Generalizable Gaussian Splatting Reconstruction from Multi-View Stereo [54.00987996368157]
  We present MVSGaussian, a new generalizable 3D Gaussian representation approach derived from Multi-View Stereo (MVS) MVSGaussian achieves real-time rendering with better synthesis quality for each scene.
  arXiv  Detail & Related papers  (2024-05-20T17:59:30Z)
• InstantSplat: Sparse-view Gaussian Splatting in Seconds [91.77050739918037]
  We introduce InstantSplat, a novel approach for addressing sparse-view 3D scene reconstruction at lightning-fast speed.<n>InstantSplat employs a self-supervised framework that optimize 3D scene representation and camera poses.<n>It achieves an acceleration of over 30x in reconstruction and improves visual quality (SSIM) from 0.3755 to 0.7624 compared to traditional SfM with 3D-GS.
  arXiv  Detail & Related papers  (2024-03-29T17:29:58Z)
• S^2Former-OR: Single-Stage Bi-Modal Transformer for Scene Graph Generation in OR [50.435592120607815]
  Scene graph generation (SGG) of surgical procedures is crucial in enhancing holistically cognitive intelligence in the operating room (OR) Previous works have primarily relied on multi-stage learning, where the generated semantic scene graphs depend on intermediate processes with pose estimation and object detection. In this study, we introduce a novel single-stage bi-modal transformer framework for SGG in the OR, termed S2Former-OR.
  arXiv  Detail & Related papers  (2024-02-22T11:40:49Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.