We study the problem of novel view synthesis from sparse source observations
of a scene comprised of 3D objects. We propose a simple yet effective approach
that is neither continuous nor implicit, challenging recent trends on view
synthesis. Our approach explicitly encodes observations into a volumetric
representation that enables amortized rendering. We demonstrate that although
continuous radiance field representations have gained a lot of attention due to
their expressive power, our simple approach obtains comparable or even better
novel view reconstruction quality comparing with state-of-the-art baselines
while increasing rendering speed by over 400x. Our model is trained in a
category-agnostic manner and does not require scene-specific optimization.
Therefore, it is able to generalize novel view synthesis to object categories
not seen during training. In addition, we show that with our simple
formulation, we can use view synthesis as a self-supervision signal for
efficient learning of 3D geometry without explicit 3D supervision.

本文研究了从稀疏源观测中合成新视角的问题，提出了一种简单而有效的方法，通过将观测编码到体积表示中进行摊销渲染，并通过自我监督信号实现了对 3D 几何的有效学习。