  Recent advances in implicit neural representations have achieved impressive
results by sampling and fusing individual points along sampling rays in the
sampling space. However, due to the explosively growing sampling space, finely
representing and synthesizing detailed textures remains a challenge for
unbounded large-scale outdoor scenes. To alleviate the dilemma of using
individual points to perceive the entire colossal space, we explore learning
the surface distribution of the scene to provide structural priors and reduce
the samplable space and propose a Point Diffusion implicit Function, PDF, for
large-scale scene neural representation. The core of our method is a
large-scale point cloud super-resolution diffusion module that enhances the
sparse point cloud reconstructed from several training images into a dense
point cloud as an explicit prior. Then in the rendering stage, only sampling
points with prior points within the sampling radius are retained. That is, the
sampling space is reduced from the unbounded space to the scene surface.
Meanwhile, to fill in the background of the scene that cannot be provided by
point clouds, the region sampling based on Mip-NeRF 360 is employed to model
the background representation. Expensive experiments have demonstrated the
effectiveness of our method for large-scale scene novel view synthesis, which
outperforms relevant state-of-the-art baselines.
