3D-aware Generative Adversarial Networks (GANs) have shown remarkable progress in learning to generate multi-view-consistent images and 3D geometries of scenes from collections of 2D images via neural volume rendering. Yet, the significant memory and computational costs of dense sampling in volume rendering have forced 3D GANs to adopt patch-based training or employ low-resolution rendering with post-processing 2D super resolution, which sacrifices multiview consistency and the quality of resolved geometry. Consequently, 3D GANs have not yet been able to fully resolve the rich 3D geometry present in 2D images. In this work, we propose techniques to scale neural volume rendering to the much higher resolution of native 2D images, thereby resolving fine-grained 3D geometry with unprecedented detail. Our approach employs learning-based samplers for accelerating neural rendering for 3D GAN training using up to 5 times fewer depth samples. This enables us to explicitly "render every pixel" of the full-resolution image during training and inference without post-processing superresolution in 2D. Together with our strategy to learn high-quality surface geometry, our method synthesizes high-resolution 3D geometry and strictly view-consistent images while maintaining image quality on par with baselines relying on post-processing super resolution. We demonstrate state-of-the-art 3D gemetric quality on FFHQ and AFHQ, setting a new standard for unsupervised learning of 3D shapes in 3D GANs.

翻译：3D感知生成对抗网络（3D GANs）在通过神经体渲染从二维图像集合中学习生成多视图一致的图像和场景三维几何方面取得了显著进展。然而，体渲染中密集采样的巨大内存和计算成本迫使3D GANs采用基于图块的训练或低分辨率渲染加后处理二维超分辨率技术，这牺牲了多视图一致性和解析几何的质量。因此，3D GANs尚未能完全解析二维图像中蕴含的丰富三维几何。在本工作中，我们提出将神经体渲染扩展至原生二维图像的更高分辨率，从而以前所未有的细节解析精细三维几何。我们的方法采用基于学习的采样器加速神经渲染，在3D GAN训练中最多可减少5倍的深度采样数量。这使得我们能够在训练和推理过程中显式地"渲染全分辨率图像的每个像素"，而无需二维后处理超分辨率。结合我们学习高质量表面几何的策略，该方法能够合成高分辨率三维几何与严格视图一致的图像，同时保持与依赖后处理超分辨率的基线方法相当的图像质量。我们在FFHQ和AFHQ数据集上展示了最先进的3D几何质量，为3D GANs中三维形状的无监督学习设立了新标准。