BakedSDF:用于实时视图合成的网格神经sdf

Lior Yariv, Peter Hedman, Christian Reiser, Dor Verbin, Pratul P. Srinivasan, R. Szeliski, J. Barron, B. Mildenhall
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引用次数: 31

摘要

我们提出了一种适合于真实感新视图合成的大型无界现实场景的高质量网格重建方法。我们首先优化了一个混合神经体-表面场景表示,它被设计成具有与场景中的表面对应的行为良好的水平集。然后,我们将这种表示烘烤成一个高质量的三角形网格,我们配备了一个简单而快速的基于球面高斯的视图依赖外观模型。最后,我们优化了这种烘焙表示,以最好地再现捕获的视点,从而产生一个可以利用加速多边形光栅化管道在商品硬件上进行实时视图合成的模型。我们的方法在准确性、速度和功耗方面优于以前的实时渲染场景表示,并产生高质量的网格,使外观编辑和物理模拟等应用成为可能。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
BakedSDF: Meshing Neural SDFs for Real-Time View Synthesis
We present a method for reconstructing high-quality meshes of large unbounded real-world scenes suitable for photorealistic novel view synthesis. We first optimize a hybrid neural volume-surface scene representation designed to have well-behaved level sets that correspond to surfaces in the scene. We then bake this representation into a high-quality triangle mesh, which we equip with a simple and fast view-dependent appearance model based on spherical Gaussians. Finally, we optimize this baked representation to best reproduce the captured viewpoints, resulting in a model that can leverage accelerated polygon rasterization pipelines for real-time view synthesis on commodity hardware. Our approach outperforms previous scene representations for real-time rendering in terms of accuracy, speed, and power consumption, and produces high quality meshes that enable applications such as appearance editing and physical simulation.
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