There has recently been great interest in neural rendering methods. Some
approaches use 3D geometry reconstructed with Multi-View Stereo (MVS) but
cannot recover from the errors of this process, while others directly learn a
volumetric neural representation, but suffer from expensive training and
inference. We introduce a general approach that is initialized with MVS, but
allows further optimization of scene properties in the space of input views,
including depth and reprojected features, resulting in improved novel-view
synthesis. A key element of our approach is our new differentiable point-based
pipeline, based on bi-directional Elliptical Weighted Average splatting, a
probabilistic depth test and effective camera selection. We use these elements
together in our neural renderer, that outperforms all previous methods both in
quality and speed in almost all scenes we tested. Our pipeline can be applied
to multi-view harmonization and stylization in addition to novel-view
synthesis.

介绍了一种基于多视角渲染的新型的神经渲染方法，该方法可以进一步优化场景属性，包括深度和重投影特征，从而实现改善的新视图合成。