从斑块到辐条：通过定向高斯实现高保真的表面重建
From Blobs to Spokes: High-Fidelity Surface Reconstruction via Oriented Gaussians
AI总结：
本文提出Gaussian Wrapping方法，通过引入可学习的定向法向量和改进的衰减公式，实现复杂场景的高精度封闭网格重建，解决了传统3DGS在表面提取中的难题。
摘要：
3D高斯点云（3DGS）已革新了快速生成新视角合成，但其基于不透明度的公式使表面提取变得根本困难。与基于签名距离场或占位的隐式方法不同，3DGS缺乏全局几何场，迫使现有方法依赖于诸如混合深度图的TSDF融合等启发式方法。受Objects as Volumes框架的启发，我们为高斯点云推导出一个原理性的占位场，并展示如何利用它提取复杂场景的高精度封闭网格。我们的关键贡献是为每个高斯元素引入可学习的定向法向量，并定义适应的衰减公式，从而在空间任意位置获得法向量和占位场的闭式表达。我们进一步引入了新的一致性损失和专门的密集化策略，迫使高斯体包裹整个表面以封闭几何孔洞，确保一个完整的定向原始体壳。我们修改可微分光栅化器以输出深度作为我们连续模型的等值面，并引入Primal Adaptive Meshing进行任意分辨率的感兴趣区域网格化。我们还揭示了标准表面评估协议中的基本偏差，并提出两种更严格的替代方案。总体而言，我们的方法Gaussian Wrapping在DTU和Tanks and Temples数据集上达到了新的状态-of-the-art，生成了完整、封闭的网格，其大小仅为现有工作的几分之一，能够恢复如 notoriously elusive bicycle spokes 这样细长的结构。
英文摘要：
3D Gaussian Splatting (3DGS) has revolutionized fast novel view synthesis, yet its opacity-based formulation makes surface extraction fundamentally difficult. Unlike implicit methods built on Signed Distance Fields or occupancy, 3DGS lacks a global geometric field, forcing existing approaches to resort to heuristics such as TSDF fusion of blended depth maps. Inspired by the Objects as Volumes framework, we derive a principled occupancy field for Gaussian Splatting and show how it can be used to extract highly accurate watertight meshes of complex scenes. Our key contribution is to introduce a learnable oriented normal at each Gaussian element and to define an adapted attenuation formulation, which leads to closed-form expressions for both the normal and occupancy fields at arbitrary locations in space. We further introduce a novel consistency loss and a dedicated densification strategy to enforce Gaussians to wrap the entire surface by closing geometric holes, ensuring a complete shell of oriented primitives. We modify the differentiable rasterizer to output depth as an isosurface of our continuous model, and introduce Primal Adaptive Meshing for Region-of-Interest meshing at arbitrary resolution. We additionally expose fundamental biases in standard surface evaluation protocols and propose two more rigorous alternatives. Overall, our method Gaussian Wrapping sets a new state-of-the-art on DTU and Tanks and Temples, producing complete, watertight meshes at a fraction of the size of concurrent work-recovering thin structures such as the notoriously elusive bicycle spokes.
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