基于几何分解与多分辨率低秩特征的快速隐式神经光场表示

中文摘要 AI 辅助

隐式神经表示为从采样光线坐标重建密集光场提供了一种紧凑且连续的方式。然而，快速光场重建仍具挑战性，因为光场是一种高维信号，具有强空间-角度冗余和结构化视差变化。直接用神经网络拟合4D光线坐标通常需要大量优化时间来恢复视图外观和跨视图一致性。为解决该问题，本文提出一种基于几何分解与多分辨率低秩特征的快速隐式光场表示方法。该方法将4D光场分解为水平视差平面、空间纹理平面和垂直视差平面，每个平面由低秩结构表示，该结构结合了低分辨率2D网格与多分辨率级别下两个高分辨率1D线特征的逐元素乘积。融合后的特征由轻量级多层感知机解码以预测RGB值。在公开光场数据集上的实验表明，所提方法在达到有竞争力的重建质量的同时，在模型参数、训练时间和推理效率之间实现了更优的权衡。

英文摘要

Implicit neural representations provide a compact and continuous way to reconstruct dense light fields from sampled ray coordinates. However, fast light field reconstruction remains challenging because a light field is a high-dimensional signal with strong spatial-angular redundancy and structured disparity variations. Directly fitting 4D ray coordinates with a neural network often requires considerable optimization time to recover both view appearance and cross-view consistency. To address this issue, this paper proposes a fast implicit light field representation based on geometric decomposition and multi-resolution low-rank features. The proposed method decomposes a 4D light field into a horizontal disparity plane, a spatial texture plane, and a vertical disparity plane. Each plane is represented by a low-rank structure that combines a low-resolution 2D grid with the element-wise product of two high-resolution 1D line features at multiple resolution levels. The fused features are decoded by a lightweight multilayer perceptron to predict RGB values. Experiments on public light field datasets show that the proposed method achieves competitive reconstruction quality while providing a better trade-off among model parameters, training time, and inference efficiency.

发表机构

• Institute of Computational Imaging, Beijing Information Science and Technology University（北京信息科技大学计算成像研究所）
• College of Computer Science (College of Software), Inner Mongolia University（内蒙古大学计算机学院（软件学院））

机构由 AI 辅助整理，请以论文原文为准。

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备注

6 pages, 2 figures, submitted to IEEE CYBER 2026