In this work, we address the lack of 3D understanding of generative neural
networks by introducing a persistent 3D feature embedding for view synthesis.
To this end, we propose DeepVoxels, a learned representation that encodes the
view-dependent appearance of a 3D scene without having to explicitly model its
geometry. At its core, our approach is based on a Cartesian 3D grid of
persistent embedded features that learn to make use of the underlying 3D scene
structure. Our approach combines insights from 3D geometric computer vision
with recent advances in learning image-to-image mappings based on adversarial
loss functions. DeepVoxels is supervised, without requiring a 3D reconstruction
of the scene, using a 2D re-rendering loss and enforces perspective and
multi-view geometry in a principled manner. We apply our persistent 3D scene
representation to the problem of novel view synthesis demonstrating
high-quality results for a variety of challenging scenes.

针对生成神经网络在 3D 理解方面的缺陷，本研究提出了一个持久的三维特征嵌入方法 ——DeepVoxels，通过学习表示对 3D 场景的视角依赖外观，无需显式地建模其几何形状。该方法结合了 3D 几何计算机视觉的见解和基于敌对性损失函数的学习图像到图像的映射的最新进展。DeepVoxels 是有监督的，无需对场景进行 3D 重构，使用 2D 重新渲染损失，并以原则方式强制执行透视和多视角几何。同时应用于新颖视图综合问题，展示了对各种具有挑战性的场景的高质量结果。