Neural Radiance Fields (NeRFs) have proven to be powerful 3D representations, capable of high quality novel view synthesis of complex scenes. While NeRFs have been applied to graphics, vision, and robotics, problems with slow rendering speed and characteristic visual artifacts prevent adoption in many use cases. In this work, we investigate combining an autoencoder (AE) with a NeRF, in which latent features (instead of colours) are rendered and then convolutionally decoded. The resulting latent-space NeRF can produce novel views with higher quality than standard colour-space NeRFs, as the AE can correct certain visual artifacts, while rendering over three times faster. Our work is orthogonal to other techniques for improving NeRF efficiency. Further, we can control the tradeoff between efficiency and image quality by shrinking the AE architecture, achieving over 13 times faster rendering with only a small drop in performance. We hope that our approach can form the basis of an efficient, yet high-fidelity, 3D scene representation for downstream tasks, especially when retaining differentiability is useful, as in many robotics scenarios requiring continual learning.

翻译：神经辐射场（NeRFs）已被证明是强大的三维表示方法，能够对复杂场景实现高质量的新视角合成。尽管NeRFs已应用于图形学、视觉和机器人学领域，但其渲染速度慢和特有的视觉伪影问题限制了其在许多场景中的采纳。在本工作中，我们研究了将自编码器（AE）与NeRF相结合的方法，其中渲染的是潜在特征（而非颜色值），随后通过卷积解码器进行解码。由此产生的潜空间NeRF能够比标准颜色空间NeRF生成更高质量的新视角图像，这是因为自编码器可以纠正某些视觉伪影，同时渲染速度提升了三倍以上。我们的工作与其它提升NeRF效率的技术相互正交。此外，通过缩小自编码器架构，我们可以控制效率与图像质量之间的权衡，在仅带来轻微性能下降的情况下实现超过13倍的渲染加速。我们希望我们的方法能够为下游任务（尤其是需要保持可微性的场景，如许多需要持续学习的机器人学任务）奠定高效且高保真三维场景表示的基础。