  Existing state-of-the-art novel view synthesis methods rely on either fairly
accurate 3D geometry estimation or sampling of the entire space for neural
volumetric rendering, which limit the overall efficiency. In order to improve
the rendering efficiency by reducing sampling points without sacrificing
rendering quality, we propose to build a novel view synthesis framework based
on learned MVS priors that enables general, fast and photo-realistic view
synthesis simultaneously. Specifically, fewer but important points are sampled
under the guidance of depth probability distributions extracted from the
learned MVS architecture. Based on the learned probability-guided sampling, a
neural volume rendering module is elaborately devised to fully aggregate source
view information as well as the learned scene structures to synthesize
photorealistic target view images. Finally, the rendering results in uncertain,
occluded and unreferenced regions can be further improved by incorporating a
confidence-aware refinement module. Experiments show that our method achieves
15 to 40 times faster rendering compared to state-of-the-art baselines, with
strong generalization capacity and comparable high-quality novel view synthesis
performance.
