We introduce a method for high-quality 3D reconstruction from multi-view images. Our method uses a new point-based representation, the regularized dipole sum, which generalizes the winding number to allow for interpolation of per-point attributes in point clouds with noisy or outlier points. Using regularized dipole sums, we represent implicit geometry and radiance fields as per-point attributes of a dense point cloud, which we initialize from structure from motion. We additionally derive Barnes-Hut fast summation schemes for accelerated forward and adjoint dipole sum queries. These queries facilitate the use of ray tracing to efficiently and differentiably render images with our point-based representations, and thus update their point attributes to optimize scene geometry and appearance. We evaluate our method in inverse rendering applications against state-of-the-art alternatives, based on ray tracing of neural representations or rasterization of Gaussian point-based representations. Our method significantly improves 3D reconstruction quality and robustness at equal runtimes, while also supporting more general rendering methods such as shadow rays for direct illumination.

中文翻译：

---

使用快速偶极子和进行 3D 重建


我们介绍了一种从多视图图像进行高质量 3D 重建的方法。我们的方法使用了一种新的基于点的表示，即正则化偶极子和，它泛化了绕组数，以允许在具有噪声或异常点的点云中对每个点的属性进行插值。使用正则化偶极子和，我们将隐式几何和辐射场表示为密集点云的每点属性，我们从运动的结构中初始化。我们还推导出了用于加速前向和伴随偶极子和查询的 Barnes-Hut 快速求和方案。这些查询有助于使用光线追踪，以基于点的表示形式高效、差异化地渲染图像，从而更新它们的点属性以优化场景几何体和外观。我们基于神经表示的光线追踪或基于高斯点的表示的光栅化，将我们在反向渲染应用程序中的方法与最先进的替代方案进行比较。我们的方法在相同的运行时显著提高了 3D 重建质量和稳健性，同时还支持更通用的渲染方法，例如用于直接照明的阴影光线。