Fast, reliable shape reconstruction is an essential ingredient in many computer vision applications. Neural Radiance Fields demonstrated that photorealistic novel view synthesis is within reach, but was gated by performance requirements for fast reconstruction of real scenes and objects. Several recent approaches have built on alternative shape representations, in particular, 3D Gaussians. We develop extensions to these renderers, such as integrating differentiable optical flow, exporting watertight meshes and rendering per-ray normals. Additionally, we show how two of the recent methods are interoperable with each other. These reconstructions are quick, robust, and easily performed on GPU or CPU. For code and visual examples, see https://leonidk.github.io/fmb-plus

快速可靠的形状重建是许多计算机视觉应用的关键要素。神经辐射场证明了逼真的新视角合成是可行的，但在对实景和物体进行快速重建方面要求表现。最近的几种方法基于其他形状表示，特别是3D高斯函数。我们对这些渲染器进行扩展，例如集成可微分光流、导出满封闭网格和按射线渲染法线。此外，我们展示了两种最近方法之间的互操作性。这些重建速度快、鲁棒，并且可以在GPU或CPU上轻松执行。详细的代码和可视化示例请参见网址：https://此处为网址

3D高斯差分渲染的灵活技术