发表机构
Zhejiang University; Shenzhen University（浙江大学; 深圳大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
InfiniSplat是一种前馈单图像3DGS框架，通过几何引导采样和查询条件隐式解码器实现表面对齐表示，在跨数据集NVS任务中达SOTA，且具零样本泛化能力。
AI 中文摘要
单图像前馈三维高斯溅射（3DGS）旨在从单张输入图像直接生成可渲染的三维场景表示，避免了多视图采集和逐场景优化的成本。然而，现有方法常受限于像素对齐的表示，高斯分布从固定图像网格位置预测。这类像素对齐基元可生成有前景的近视图渲染，但与底层场景表面耦合较弱，在大视角偏移下难以保持连贯结构。我们提出InfiniSplat，一种前馈单图像3DGS框架，从像素对齐表示转向表面对齐表示。InfiniSplat通过几何引导采样，根据深度诱导的局部表面结构放置二维支撑点，再应用查询条件隐式解码器，从这些支撑点查询到的图像特征预测高斯属性。通过在几何中锚定支撑点，同时将高斯预测与固定像素中心解耦，InfiniSplat生成的高斯布局更贴合场景表面，减少了网格导致的分散基元。在多个跨数据集新视图合成（NVS）评估中，InfiniSplat相比单图像前馈基线达到了最先进性能，并展示了从Hypersim室内合成训练到复杂开放世界场景的零样本泛化能力。
英文摘要
Single-image feed-forward 3D Gaussian Splatting (3DGS) aims to directly generate a renderable 3D scene representation from one input image, avoiding the cost of multi-view capture and per-scene optimization. However, existing methods are often constrained by a pixel-aligned representation, where Gaussians are predicted from fixed image-grid locations. Such pixel-aligned primitives can produce promising nearby-view renderings, but they remain weakly coupled to underlying scene surfaces and struggle to preserve coherent structures under large viewpoint shifts. We present InfiniSplat, a feed-forward single-image 3DGS framework that moves from a pixel-aligned representation toward a surface-aligned representation. InfiniSplat constructs this representation by first using geometry-guided sampling to place 2D supports according to depth-induced local surface structure, and then applying a query-conditioned implicit decoder to predict Gaussian attributes from the image features queried at these supports. By grounding support locations in geometry while decoupling Gaussian prediction from fixed pixel centers, InfiniSplat produces Gaussian layouts that better follow scene surfaces and reduce scattered primitives caused by grid discretization. Across multiple cross-dataset NVS evaluations, InfiniSplat achieves state-of-the-art performance compared with single-image feed-forward baselines, and demonstrates zero-shot generalization from Hypersim indoor synthetic training to complex open-world scenes. Project page: https://zju3dv.github.io/InfiniSplat.
CommentsAccepted to SIGGRAPH Asia 2026 (Journal Track). Code: https://github.com/zju3dv/InfiniSplat
