论文标题
神经：平面专家的神经混合物查看合成
NeurMiPs: Neural Mixture of Planar Experts for View Synthesis
论文作者
论文摘要
我们提出了平面专家（Neurmips）的神经混合物，这是一种基于平面的新型场景表示，用于建模几何和外观。 Neurmips利用3D空间中的本地平面专家作为场景表示。每个平面专家都由代表几何形状的局部矩形形状的参数和对颜色和不透明度建模的神经辐射场组成。我们通过计算射线平面相交，复合输出颜色以及相交点的密度来呈现新的视图。 Neurmips融合了神经辐射场的显式网格渲染和灵活性的效率。与新型观点合成中的其他3D表示相比，实验证明了我们提出的方法的卓越性能和速度。
We present Neural Mixtures of Planar Experts (NeurMiPs), a novel planar-based scene representation for modeling geometry and appearance. NeurMiPs leverages a collection of local planar experts in 3D space as the scene representation. Each planar expert consists of the parameters of the local rectangular shape representing geometry and a neural radiance field modeling the color and opacity. We render novel views by calculating ray-plane intersections and composite output colors and densities at intersected points to the image. NeurMiPs blends the efficiency of explicit mesh rendering and flexibility of the neural radiance field. Experiments demonstrate superior performance and speed of our proposed method, compared to other 3D representations in novel view synthesis.
