This paper introduces a novel paradigm for the generalizable neural radiance
field (NeRF). Previous generic NeRF methods combine multiview stereo techniques
with image-based neural rendering for generalization, yielding impressive
results, while suffering from three issues. First, occlusions often result in
inconsistent feature matching. Then, they deliver distortions and artifacts in
geometric discontinuities and locally sharp shapes due to their individual
process of sampled points and rough feature aggregation. Third, their
image-based representations experience severe degradations when source views
are not near enough to the target view. To address challenges, we propose the
first paradigm that constructs the generalizable neural field based on
point-based rather than image-based rendering, which we call the Generalizable
neural Point Field (GPF). Our approach explicitly models visibilities by
geometric priors and augments them with neural features. We propose a novel
nonuniform log sampling strategy to improve both rendering speed and
reconstruction quality. Moreover, we present a learnable kernel spatially
augmented with features for feature aggregations, mitigating distortions at
places with drastically varying geometries. Besides, our representation can be
easily manipulated. Experiments show that our model can deliver better
geometries, view consistencies, and rendering quality than all counterparts and
benchmarks on three datasets in both generalization and finetuning settings,
preliminarily proving the potential of the new paradigm for generalizable NeRF.

提出了一种新型的泛化神经辐射场（NeRF）范式，通过基于点而非基于图像的渲染构建了可泛化的神经场，称之为可泛化神经点场（GPF）。该模型通过显式建模可见性并将其与神经特征相结合，提出了一种非均匀取样策略以提高渲染速度和重构质量，并利用特征增强的可学习内核在具有差异性几何结构的区域缓解形变。该研究表明，相比于其他方法和基准数据集，该模型在三个数据集上无论在泛化还是微调设置下，都能提供更好的几何形状、视角一致性和渲染质量，初步证明了该新范式在泛化 NeRF 中的潜力。