InfiniSplat: Implicit Gaussian Decoding for Large-Baseline Monocular View Synthesis

• URL: http://arxiv.org/abs/2608.02437v2
• Date: Tue, 04 Aug 2026 03:15:02 GMT
• Title: InfiniSplat: Implicit Gaussian Decoding for Large-Baseline Monocular View Synthesis
• Abstract summary: InfiniSplat is a feed-forward single-image 3DGS framework that moves from a pixel-aligned representation toward a surface-aligned representation.<n>InfiniSplat achieves state-of-the-art performance compared with single-image feed-forward baselines.
• Score: 49.87457834078904
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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.

Related papers

• PointSplat: Compact Gaussian Splatting via Human-Centric Prediction [70.45838533838104]
  We propose PointSplat, a novel human-centric approach to producing 3D human representations from input views on the fly.<n>The proposed method first estimates a coarse geometric proxy and performs ray casting to prune redundant points.<n>It then employs a Point-Image Transformer to fuse appearance and geometry features, predicting Gaussian attributes in a single forward pass.
  arXiv  Detail & Related papers  (2026-06-30T17:59:05Z)
• FLAT: Feedforward Latent Triangle Splatting for Geometrically Accurate Scene Generation [72.38727895659405]
  We introduce FLAT, which decodes triangle splats directly from video diffusion latents.<n>On standard benchmarks, FLAT achieves significantly better geometric accuracy compared to state-of-the-art feedforward baselines.<n>We show that a lightweight test-time refinement step converts the predicted triangle soup into a fully opaque, game-engine-ready representation.
  arXiv  Detail & Related papers  (2026-06-23T17:53:41Z)
• 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)
• AnchorSplat: Feed-Forward 3D Gaussian Splatting with 3D Geometric Priors [23.943522711585597]
  We propose AnchorSplat, a novel feed-forward 3DGS framework for scene-level reconstruction.<n> anchor-aligned Gaussian representation guided by 3D geometric priors.<n>Design substantially reduces the number of required Gaussians.
  arXiv  Detail & Related papers  (2026-04-08T13:04:54Z)
• Triangle Splatting+: Differentiable Rendering with Opaque Triangles [54.18495204764292]
  We introduce Triangle Splatting+, which directly optimize triangles within a differentiable splatting framework.<n>Our method surpasses prior splatting approaches in visual fidelity while remaining efficient and fast to training.<n>The resulting semi-connected meshes support downstream applications such as physics-based simulation or interactive walkthroughs.
  arXiv  Detail & Related papers  (2025-09-29T17:43:46Z)
• VolSplat: Rethinking Feed-Forward 3D Gaussian Splatting with Voxel-Aligned Prediction [45.95623374754385]
  VolSplat is a new multi-view feed-forward paradigm that replaces pixel alignment with voxel-aligned Gaussians.<n>It overcomes pixel alignment's reliance on error-prone 2D feature matching, ensuring robust multi-view consistency.<n> Experiments on widely used benchmarks including RealEstate10K and ScanNet demonstrate that VolSplat achieves state-of-the-art performance.
  arXiv  Detail & Related papers  (2025-09-23T17:59:02Z)
• AnySplat: Feed-forward 3D Gaussian Splatting from Unconstrained Views [68.94737256959661]
  AnySplat is a feed forward network for novel view synthesis from uncalibrated image collections.<n>A single forward pass yields a set of 3D Gaussian primitives encoding both scene geometry and appearance.<n>In extensive zero shot evaluations, AnySplat matches the quality of pose aware baselines in both sparse and dense view scenarios.
  arXiv  Detail & Related papers  (2025-05-29T17:49:56Z)
• CATSplat: Context-Aware Transformer with Spatial Guidance for Generalizable 3D Gaussian Splatting from A Single-View Image [18.445769892372528]
  We introduce CATSplat, a novel generalizable transformer-based framework for single-view 3D scene reconstruction.<n>By incorporating scene-specific contextual details from text embeddings through cross-attention, we pave the way for context-aware reconstruction.<n>Experiments on large-scale datasets demonstrate the state-of-the-art performance of CATSplat in single-view 3D scene reconstruction.
  arXiv  Detail & Related papers  (2024-12-17T13:32:04Z)
• HiSplat: Hierarchical 3D Gaussian Splatting for Generalizable Sparse-View Reconstruction [46.269350101349715]
  HiSplat is a novel framework for generalizable 3D Gaussian Splatting. It generates hierarchical 3D Gaussians via a coarse-to-fine strategy. It significantly enhances reconstruction quality and cross-dataset generalization.
  arXiv  Detail & Related papers  (2024-10-08T17:59:32Z)
• FreeSplat: Generalizable 3D Gaussian Splatting Towards Free-View Synthesis of Indoor Scenes [50.534213038479926]
  FreeSplat is capable of reconstructing geometrically consistent 3D scenes from long sequence input towards free-view synthesis. We propose a simple but effective free-view training strategy that ensures robust view synthesis across broader view range regardless of the number of views.
  arXiv  Detail & Related papers  (2024-05-28T08:40:14Z)
• Hybrid Explicit Representation for Ultra-Realistic Head Avatars [55.829497543262214]
  We introduce a novel approach to creating ultra-realistic head avatars and rendering them in real-time.<n> UV-mapped 3D mesh is utilized to capture sharp and rich textures on smooth surfaces, while 3D Gaussian Splatting is employed to represent complex geometric structures.<n>Experiments that our modeled results exceed those of state-of-the-art approaches.
  arXiv  Detail & Related papers  (2024-03-18T04:01:26Z)

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.