NeRF is a popular model that efficiently represents 3D objects from 2D
images. However, vanilla NeRF has a few important limitations. NeRF must be
trained on each object separately. The training time is long since we encode
the object's shape and color in neural network weights. Moreover, NeRF does not
generalize well to unseen data. In this paper, we present MultiPlaneNeRF -- a
first model that simultaneously solves all the above problems. Our model works
directly on 2D images. We project 3D points on 2D images to produce
non-trainable representations. The projection step is not parametrized, and a
very shallow decoder can efficiently process the representation. Using existing
images as part of NeRF can significantly reduce the number of parameters since
we train only a small implicit decoder. Furthermore, we can train
MultiPlaneNeRF on a large data set and force our implicit decoder to generalize
across many objects. Consequently, we can only replace the 2D images (without
additional training) to produce a NeRF representation of the new object. In the
experimental section, we demonstrate that MultiPlaneNeRF achieves comparable
results to state-of-the-art models for synthesizing new views and has
generalization properties.

本文介绍了 MultiPlaneNeRF 模型，该模型可从 2D 图像中直接产生非可训练的表示，并可通过大量数据的训练来实现推广，以及在生成新视角方面达到业界领先水平。