We present a method that synthesizes novel views of complex scenes by
interpolating a sparse set of nearby views. The core of our method is a network
architecture that includes a multilayer perceptron and a ray transformer that
estimates radiance and volume density at continuous 5D locations (3D spatial
locations and 2D viewing directions), drawing appearance information on the fly
from multiple source views. By drawing on source views at render time, our
method hearkens back to classic work on image-based rendering (IBR), and allows
us to render high-resolution imagery. Unlike neural scene representation work
that optimizes per-scene functions for rendering, we learn a generic view
interpolation function that generalizes to novel scenes. We render images using
classic volume rendering, which is fully differentiable and allows us to train
using only multi-view posed images as supervision. Experiments show that our
method outperforms recent novel view synthesis methods that also seek to
generalize to novel scenes. Further, if fine-tuned on each scene, our method is
competitive with state-of-the-art single-scene neural rendering methods.
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通过绘制多种源视图来渲染高分辨率成像，并利用神经网路的视角插值，在新颖场景中综合新视角。