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SurfaceSplat: Connecting Surface Reconstruction and Gaussian Splatting

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arxiv 2507.15602 v2 pith:FD2I5I7B submitted 2025-07-21 cs.CV

classification cs.CV
keywords reconstructionsurfaceapproachesnoveldetailsgaussiangeometryimages
verification ladder T0 review T1 audit T2 compute T3 formal
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Surface reconstruction and novel view rendering from sparse-view images are challenging. Signed Distance Function (SDF)-based methods struggle with fine details, while 3D Gaussian Splatting (3DGS)-based approaches lack global geometry coherence. We propose a novel hybrid method that combines the strengths of both approaches: SDF captures coarse geometry to enhance 3DGS-based rendering, while newly rendered images from 3DGS refine the details of SDF for accurate surface reconstruction. As a result, our method surpasses state-of-the-art approaches in surface reconstruction and novel view synthesis on the DTU and MobileBrick datasets. Code will be released at https://github.com/aim-uofa/SurfaceSplat.

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Cited by 2 Pith papers

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score.

  1. TriSplat: Simulation-Ready Feed-Forward 3D Scene Reconstruction

    cs.CV 2026-05 unverdicted novelty 6.0 of 10

    TriSplat predicts oriented triangle primitives from images in one forward pass to produce simulation-ready 3D meshes with competitive rendering quality.

  2. Feed-Forward 3D Scene Modeling: A Problem-Driven Perspective

    cs.CV 2026-04 unverdicted novelty 6.0 of 10

    The paper proposes a problem-driven taxonomy for feed-forward 3D scene modeling that groups methods by five core challenges: feature enhancement, geometry awareness, model efficiency, augmentation strategies, and temp...

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