We introduce Neural Point Light Fields that represent scenes implicitly with
a light field living on a sparse point cloud. Combining differentiable volume
rendering with learned implicit density representations has made it possible to
synthesize photo-realistic images for novel views of small scenes. As neural
volumetric rendering methods require dense sampling of the underlying
functional scene representation, at hundreds of samples along a ray cast
through the volume, they are fundamentally limited to small scenes with the
same objects projected to hundreds of training views. Promoting sparse point
clouds to neural implicit light fields allows us to represent large scenes
effectively with only a single radiance evaluation per ray. These point light
fields are a function of the ray direction, and local point feature
neighborhood, allowing us to interpolate the light field conditioned training
images without dense object coverage and parallax. We assess the proposed
method for novel view synthesis on large driving scenarios, where we synthesize
realistic unseen views that existing implicit approaches fail to represent. We
validate that Neural Point Light Fields make it possible to predict videos
along unseen trajectories previously only feasible to generate by explicitly
modeling the scene.

采用神经隐式点光场方法实现体积渲染，通过几百个样本进行射线投射，通过点云实现大场景动态渲染视频，与现有的方法相比，新方法的视角预测能力更强。