◇ T3lescope: Arbitrary-Resolution High-Fidelity Generative Surface Reconstruction from Images

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    "summary": "We reconstruct high-fidelity 3D scene meshes from posed multi-view images without per-scene optimization, across scales ranging from single objects to large outdoor scenes. Per-scene optimization methods lack the learned 3D prior needed when observations are sparse or surfaces are glossy or transparent. Existing generative methods leverage such priors to complete geometry in sparsely observed regi",
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      "作者 Atsuhiro Noguchi、Tianhan Xu、Yiming Liang、Yuta Kikuchi、Masahiro Ishiyama、Shintaro Takagi",
      "发布 2026-10-02T00:00:00.000Z"
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        "Atsuhiro Noguchi",
        "Tianhan Xu",
        "Yiming Liang",
        "Yuta Kikuchi",
        "Masahiro Ishiyama",
        "Shintaro Takagi",
        "Hitoshi Murai",
        "Eiichi Matsumoto"
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          "Yiming Liang",
          "Yuta Kikuchi",
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          "Shintaro Takagi",
          "Hitoshi Murai",
          "Eiichi Matsumoto"
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        "summary": "We reconstruct high-fidelity 3D scene meshes from posed multi-view images without per-scene optimization, across scales ranging from single objects to large outdoor scenes. Per-scene optimization methods lack the learned 3D prior needed when observations are sparse or surfaces are glossy or transparent. Existing generative methods leverage such priors to complete geometry in sparsely observed regions, but typically operate at a fixed resolution over a limited spatial extent, trading spatial coverage against detail. Reconstructing a large scene therefore often requires partitioning it into inde",
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