We propose LIRF (Local Implicit Ray Function), a generalizable neural
rendering approach for novel view rendering. Current generalizable neural
radiance fields (NeRF) methods sample a scene with a single ray per pixel and
may therefore render blurred or aliased views when the input views and rendered
views capture scene content with different resolutions. To solve this problem,
we propose LIRF to aggregate the information from conical frustums to construct
a ray. Given 3D positions within conical frustums, LIRF takes 3D coordinates
and the features of conical frustums as inputs and predicts a local volumetric
radiance field. Since the coordinates are continuous, LIRF renders high-quality
novel views at a continuously-valued scale via volume rendering. Besides, we
predict the visible weights for each input view via transformer-based feature
matching to improve the performance in occluded areas. Experimental results on
real-world scenes validate that our method outperforms state-of-the-art methods
on novel view rendering of unseen scenes at arbitrary scales.

本论文提出一种通用神经渲染方法 ——LIRF (Local Implicit Ray Function)，其通过聚合锥体视锥的信息来构建一条光线，接收连续的 3D 坐标和锥体视锥的特征作为输入，预测局部体积辐射场，从而能够以连续值的规模通过体渲染高质量的新视图。同时，使用基于变压器的特征匹配预测每个输入视图的可见权重，以提高在遮挡区域的性能，实验结果验证了我们的方法在未见过的场景的新视图渲染上优于现有技术。