Contemporary 3D research, particularly in reconstruction and generation, heavily relies on 2D images for inputs or supervision. However, current designs for these 2D-3D mapping are memory-intensive, posing a significant bottleneck for existing methods and hindering new applications. In response, we propose a pair of highly scalable components for 3D neural fields: Lightplane Render and Splatter, which significantly reduce memory usage in 2D-3D mapping. These innovations enable the processing of vastly more and higher resolution images with small memory and computational costs. We demonstrate their utility in various applications, from benefiting single-scene optimization with image-level losses to realizing a versatile pipeline for dramatically scaling 3D reconstruction and generation. Code: \url{https://github.com/facebookresearch/lightplane}.

翻译：当代三维研究，特别是在重建与生成领域，高度依赖二维图像作为输入或监督信号。然而，当前用于这种二维-三维映射的设计存在内存密集型问题，这已成为现有方法的主要瓶颈，并阻碍了新应用的开发。针对这一问题，我们提出了一对用于三维神经场的高可扩展组件：Lightplane渲染器与Lightplane溅射器，它们显著降低了二维-三维映射中的内存消耗。这些创新使得仅需较小的内存与计算成本即可处理数量更多、分辨率更高的图像。我们展示了这些组件在多种应用中的实用性，从通过图像级损失优化单场景，到实现可大幅扩展三维重建与生成的多功能流水线。代码：\url{https://github.com/facebookresearch/lightplane}。