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.

通过学习场景的表面分布、减小可采样空间并采用 Point Diffusion implicit Function (PDF) 方法，实现了大规模场景的神经表示，其中包括利用扩散模块将稀疏点云转化为密集点云作为先验，并通过基于 Mip-NeRF 360 的区域采样来填充场景的背景，该方法在大规模场景的新视角合成中效果显著优于相关最先进技术。

大规模场景神经表示的点扩散隐式函数

PDF: Point Diffusion Implicit Function for Large-scale Scene Neural  Representation

Augmentation techniques and sampling strategies are crucial in contrastive
learning, but in most existing works, augmentation techniques require careful
design, and their sampling strategies can only capture a small amount of
intrinsic supervision information. Additionally, the existing methods require
complex designs to obtain two different representations of the data. To
overcome these limitations, we propose a novel framework called the
Self-Contrastive Graph Diffusion Network (SCGDN). Our framework consists of two
main components: the Attentional Module (AttM) and the Diffusion Module (DiFM).
AttM aggregates higher-order structure and feature information to get an
excellent embedding, while DiFM balances the state of each node in the graph
through Laplacian diffusion learning and allows the cooperative evolution of
adjacency and feature information in the graph. Unlike existing methodologies,
SCGDN is an augmentation-free approach that avoids "sampling bias" and semantic
drift, without the need for pre-training. We conduct a high-quality sampling of
samples based on structure and feature information. If two nodes are neighbors,
they are considered positive samples of each other. If two disconnected nodes
are also unrelated on $k$NN graph, they are considered negative samples for
each other. The contrastive objective reasonably uses our proposed sampling
strategies, and the redundancy reduction term minimizes redundant information
in the embedding and can well retain more discriminative information. In this
novel framework, the graph self-contrastive learning paradigm gives expression
to a powerful force. SCGDN effectively balances between preserving high-order
structure information and avoiding overfitting. The results manifest that SCGDN
can consistently generate outperformance over both the contrastive methods and
the classical methods.

我们提出了一种名为 Self-Contrastive Graph Diffusion Network（SCGDN）的新框架，其中包含 Attentional Module 和 Diffusion Module 两个主要组件，以实现高效的嵌入和节点状态的平衡，并避免采样偏见和语义漂移，综合利用结构和特征信息进行抽样，并最小化嵌入中的冗余信息以保留更有区分能力的信息。实验结果表明，SCGDN 相比于对照方法和经典方法具有更好的性能。