We present NeLF-Pro, a novel representation for modeling and reconstructing light fields in diverse natural scenes that vary in extend and spatial granularity. In contrast to previous fast reconstruction methods that represent the 3D scene globally, we model the light field of a scene as a set of local light field feature probes, parameterized with position and multi-channel 2D feature maps. Our central idea is to bake the scene's light field into spatially varying learnable representations and to query point features by weighted blending of probes close to the camera - allowing for mipmap representation and rendering. We introduce a novel vector-matrix-matrix (VMM) factorization technique that effectively represents the light field feature probes as products of core factors (i.e., VM) shared among local feature probes, and a basis factor (i.e., M) - efficiently encoding internal relationships and patterns within the scene.Experimentally, we demonstrate that NeLF-Pro significantly boosts the performance of feature grid-based representations, and achieves fast reconstruction with better rendering quality while maintaining compact modeling.

我们提出了一种用于建模和重构不同自然场景中的光场的新方法NeLF-Pro，与之前的快速重构方法不同的是，我们将场景的光场建模为一组局部光场特征探针，并使用位置和多通道2D特征图进行参数化。我们的核心思想是将场景的光场以空间变化的可学习表示融合，并通过接近相机的探针的加权融合查询点特征，从而实现多级贴图表示和渲染。我们引入了一种新颖的向量矩阵矩阵（VMM）分解技术，将光场特征探针有效地表示为局部特征探针共享的核心因子（即VM）与基础因子（即M）的乘积，高效地编码了场景内部关系和模式。实验证明，NeLF-Pro显著提升了基于特征网格的表示性能，并在保持紧凑建模的同时，实现了更好的渲染质量和快速重构。

NeLF-Pro: 神经光场探针