Light Field Networks: Neural Scene Representations with Single-Evaluation Rendering
| Autor: | Sitzmann, Vincent, Rezchikov, Semon, Freeman, William T., Tenenbaum, Joshua B., Durand, Fredo |
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| Rok vydání: | 2021 |
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| Druh dokumentu: | Working Paper |
| Popis: | Inferring representations of 3D scenes from 2D observations is a fundamental problem of computer graphics, computer vision, and artificial intelligence. Emerging 3D-structured neural scene representations are a promising approach to 3D scene understanding. In this work, we propose a novel neural scene representation, Light Field Networks or LFNs, which represent both geometry and appearance of the underlying 3D scene in a 360-degree, four-dimensional light field parameterized via a neural implicit representation. Rendering a ray from an LFN requires only a single network evaluation, as opposed to hundreds of evaluations per ray for ray-marching or volumetric based renderers in 3D-structured neural scene representations. In the setting of simple scenes, we leverage meta-learning to learn a prior over LFNs that enables multi-view consistent light field reconstruction from as little as a single image observation. This results in dramatic reductions in time and memory complexity, and enables real-time rendering. The cost of storing a 360-degree light field via an LFN is two orders of magnitude lower than conventional methods such as the Lumigraph. Utilizing the analytical differentiability of neural implicit representations and a novel parameterization of light space, we further demonstrate the extraction of sparse depth maps from LFNs. Comment: First two authors contributed equally. Project website: https://vsitzmann.github.io/lfns/ |
| Databáze: | arXiv |
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