We present a method enabling the scaling of NeRFs to learn a large number of
semantically-similar scenes. We combine two techniques to improve the required
training time and memory cost per scene. First, we learn a 3D-aware latent
space in which we train Tri-Plane scene representations, hence reducing the
resolution at which scenes are learned. Moreover, we present a way to share
common information across scenes, hence allowing for a reduction of model
complexity to learn a particular scene. Our method reduces effective per-scene
memory costs by 44% and per-scene time costs by 86% when training 1000 scenes.
Our project page can be found at this https URL .

我们提出了一种方法，实现了将 NeRFs 扩展到学习大量语义相似的场景。我们结合了两种技术来改善每个场景所需的训练时间和内存成本，通过学习三平面场景表示来降低场景学习的分辨率。此外，我们还提出了一种在场景间共享公共信息的方法，从而减少学习特定场景的模型复杂性。当训练 1000 个场景时，我们的方法可以将每个场景的内存成本降低 44％，时间成本降低 86％。