SurfSplat: Conquering Feedforward 2D Gaussian Splatting with Surface Continuity Priors

• URL: http://arxiv.org/abs/2602.02000v2
• Date: Tue, 03 Feb 2026 09:52:29 GMT
• Title: SurfSplat: Conquering Feedforward 2D Gaussian Splatting with Surface Continuity Priors
• Abstract summary: SurfSplat is a feedforward framework based on 2D Gaussian Splatting (2DGS) primitive.<n>By incorporating a surface continuity prior, SurfSplat reconstructs coherent geometry together with faithful textures.<n>High-Resolution Rendering Consistency (HRRC) is a new evaluation metric designed to evaluate high-resolution reconstruction quality.
• Score: 23.876164780443915
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• Abstract: Reconstructing 3D scenes from sparse images remains a challenging task due to the difficulty of recovering accurate geometry and texture without optimization. Recent approaches leverage generalizable models to generate 3D scenes using 3D Gaussian Splatting (3DGS) primitive. However, they often fail to produce continuous surfaces and instead yield discrete, color-biased point clouds that appear plausible at normal resolution but reveal severe artifacts under close-up views. To address this issue, we present SurfSplat, a feedforward framework based on 2D Gaussian Splatting (2DGS) primitive, which provides stronger anisotropy and higher geometric precision. By incorporating a surface continuity prior and a forced alpha blending strategy, SurfSplat reconstructs coherent geometry together with faithful textures. Furthermore, we introduce High-Resolution Rendering Consistency (HRRC), a new evaluation metric designed to evaluate high-resolution reconstruction quality. Extensive experiments on RealEstate10K, DL3DV, and ScanNet demonstrate that SurfSplat consistently outperforms prior methods on both standard metrics and HRRC, establishing a robust solution for high-fidelity 3D reconstruction from sparse inputs. Project page: https://hebing-sjtu.github.io/SurfSplat-website/

Related papers

• Surfsvr: 2D Surface Priors as 3D Geometric Regularizers for Sparse Voxel Reconstruction [56.51413081953813]
  SurfSVR is a novel sparse voxel reconstruction paradigm that treats 2D surface priors as explicit 3D geometric regularizers.<n>It consistently improves sparse voxel reconstruction across scenes with substantially different visibility and geometry characteristics.
  arXiv  Detail & Related papers  (2026-08-12T11:25:31Z)
• QuerySplat: Decoupling Geometry and Appearance Representations in 3DGS Prediction [25.05567017307621]
  We propose textbfQuerySplat, a feed-forward 3DGS framework driven by geometric priors and explicit appearance decoupling.<n>Experiments demonstrate that QuerySplat mitigates the blurry rendering issues of early query-based models.<n>On the challenging DL3DV benchmark, it achieves state-of-the-art novel view synthesis performance, with average PSNR gains of 2.30 dB and 1.04 dB over the best pose-free and pose-required baselines.
  arXiv  Detail & Related papers  (2026-08-02T12:12:02Z)
• AnchorSplat: Fast and Structure Consistent Detail Synthesis for Gaussian Splatting [45.65377534924918]
  3D Gaussian Splatting (3DGS) has emerged as a powerful representation for high-fidelity rendering.<n>Existing assets often suffer from quality bottlenecks such as missing details and texture noise.<n>We propose a novel 3D-native refinement paradigm named AnchorSplat.
  arXiv  Detail & Related papers  (2026-07-01T12:14:29Z)
• 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)
• GauSSmart: Enhanced 3D Reconstruction through 2D Foundation Models and Geometric Filtering [50.675710727721786]
  We propose GauSSmart, a hybrid method that bridges 2D foundational models and 3D Gaussian Splatting reconstruction.<n>Our approach integrates established 2D computer vision techniques, including convex filtering and semantic feature supervision.<n>We validate our approach across three datasets, where GauSSmart consistently outperforms existing Gaussian Splatting.
  arXiv  Detail & Related papers  (2025-10-16T03:38:26Z)
• 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)
• SurfaceSplat: Connecting Surface Reconstruction and Gaussian Splatting [92.13737830605902]
  Surface reconstruction and novel view rendering from sparse-view images are challenging.<n>We propose a novel hybrid method that combines the strengths of both approaches.<n>Our method surpasses state-of-the-art approaches in surface reconstruction and novel view synthesis on the DTU and MobileBrick datasets.
  arXiv  Detail & Related papers  (2025-07-21T13:25:03Z)
• SparSplat: Fast Multi-View Reconstruction with Generalizable 2D Gaussian Splatting [7.9061560322289335]
  We propose an MVS-based learning that regresses 2DGS surface parameters in a feed-forward fashion to perform 3D shape reconstruction and NVS from sparse-view images.<n>The resulting pipeline attains the state-of-the-art results on the DTU 3D reconstruction benchmark in terms of Chamfer distance to ground-truth, as-well as state-of-the-art NVS.
  arXiv  Detail & Related papers  (2025-05-04T16:33:47Z)
• EVolSplat: Efficient Volume-based Gaussian Splatting for Urban View Synthesis [61.1662426227688]
  Existing NeRF and 3DGS-based methods show promising results in achieving photorealistic renderings but require slow, per-scene optimization.<n>We introduce EVolSplat, an efficient 3D Gaussian Splatting model for urban scenes that works in a feed-forward manner.
  arXiv  Detail & Related papers  (2025-03-26T02:47:27Z)
• SelfSplat: Pose-Free and 3D Prior-Free Generalizable 3D Gaussian Splatting [4.121797302827049]
  We propose SelfSplat, a novel 3D Gaussian Splatting model designed to perform pose-free and 3D prior-free generalizable 3D reconstruction from unposed multi-view images.<n>Our model addresses these challenges by effectively integrating explicit 3D representations with self-supervised depth and pose estimation techniques.<n>To present the performance of our method, we evaluated it on large-scale real-world datasets, including RealEstate10K, ACID, and DL3DV.
  arXiv  Detail & Related papers  (2024-11-26T08:01:50Z)
• MonoGSDF: Exploring Monocular Geometric Cues for Gaussian Splatting-Guided Implicit Surface Reconstruction [86.87464903285208]
  We introduce MonoGSDF, a novel method that couples primitives with a neural Signed Distance Field (SDF) for high-quality reconstruction.<n>To handle arbitrary-scale scenes, we propose a scaling strategy for robust generalization.<n>Experiments on real-world datasets outperforms prior methods while maintaining efficiency.
  arXiv  Detail & Related papers  (2024-11-25T20:07:07Z)
• GeoSplatting: Towards Geometry Guided Gaussian Splatting for Physically-based Inverse Rendering [69.67264955234494]
  GeoSplatting is a novel approach that augments 3DGS with explicit geometry guidance for precise light transport modeling.<n>By differentiably constructing a surface-grounded 3DGS from an optimizable mesh, our approach leverages well-defined mesh normals and the opaque mesh surface.<n>This enhancement ensures precise material decomposition while preserving the efficiency and high-quality rendering capabilities of 3DGS.
  arXiv  Detail & Related papers  (2024-10-31T17:57:07Z)
• PF3plat: Pose-Free Feed-Forward 3D Gaussian Splatting [54.7468067660037]
  PF3plat sets a new state-of-the-art across all benchmarks, supported by comprehensive ablation studies validating our design choices.<n>Our framework capitalizes on fast speed, scalability, and high-quality 3D reconstruction and view synthesis capabilities of 3DGS.
  arXiv  Detail & Related papers  (2024-10-29T15:28:15Z)
• 2D Gaussian Splatting for Geometrically Accurate Radiance Fields [50.056790168812114]
  3D Gaussian Splatting (3DGS) has recently revolutionized radiance field reconstruction, achieving high quality novel view synthesis and fast rendering speed without baking.<n>We present 2D Gaussian Splatting (2DGS), a novel approach to model and reconstruct geometrically accurate radiance fields from multi-view images.<n>We demonstrate that our differentiable terms allows for noise-free and detailed geometry reconstruction while maintaining competitive appearance quality, fast training speed, and real-time rendering.
  arXiv  Detail & Related papers  (2024-03-26T17:21:24Z)
• Text-to-3D using Gaussian Splatting [18.163413810199234]
  This paper proposes GSGEN, a novel method that adopts Gaussian Splatting, a recent state-of-the-art representation, to text-to-3D generation. GSGEN aims at generating high-quality 3D objects and addressing existing shortcomings by exploiting the explicit nature of Gaussian Splatting. Our approach can generate 3D assets with delicate details and accurate geometry.
  arXiv  Detail & Related papers  (2023-09-28T16:44:31Z)

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.