This paper presents BioNeRF, a biologically plausible architecture that models scenes in a 3D representation and synthesizes new views through radiance fields. Since NeRF relies on the network weights to store the scene's 3-dimensional representation, BioNeRF implements a cognitive-inspired mechanism that fuses inputs from multiple sources into a memory-like structure, improving the storing capacity and extracting more intrinsic and correlated information. BioNeRF also mimics a behavior observed in pyramidal cells concerning contextual information, in which the memory is provided as the context and combined with the inputs of two subsequent neural models, one responsible for producing the volumetric densities and the other the colors used to render the scene. Experimental results show that BioNeRF outperforms state-of-the-art results concerning a quality measure that encodes human perception in two datasets: real-world images and synthetic data.

翻译：本文提出BioNeRF——一种具有生物合理性的架构，能够以三维表征建模场景并通过辐射场合成新视角。鉴于NeRF依赖于网络权重存储场景的三维表征，BioNeRF实现了一种受认知启发的机制，将多源输入融合至类记忆结构中，从而提升存储容量并提取更具内在关联性的信息。此外，BioNeRF模拟了锥体细胞在处理上下文信息时展现的行为：将记忆作为上下文信息，与后续两个神经模型的输入相结合——其中一个模型负责生成体密度，另一个负责生成场景渲染所需的颜色。实验结果表明，在两个数据集（真实世界图像与合成数据）上，针对编码人类感知的质量指标，BioNeRF超越了现有最优方法的性能。