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

翻译：快速、可靠的形状重建是众多计算机视觉应用中的关键要素。神经辐射场展示了逼真的新视角合成可行性，但其在真实场景和物体的快速重建中受限于性能需求。近期多项研究基于替代形状表示方法，特别是3D高斯模型。我们开发了这些渲染器的扩展功能，例如集成可微光流、导出水密网格以及渲染逐射线法向量。此外，我们展示了两种近期方法之间的互操作性。这些重建过程快速、鲁棒，且易于在GPU或CPU上执行。代码和视觉示例请参见https://leonidk.github.io/fmb-plus