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，该架构以三维表征建模场景，并通过辐射场合成新视图。鉴于NeRF依赖网络权重存储场景的三维表示，BioNeRF实现了一种受认知启发的机制，将来自多源的输入融合到类记忆结构中，从而提升存储容量并提取更具内在关联性的信息。BioNeRF还模拟了锥体细胞在处理上下文信息时表现出的行为，即记忆作为上下文提供，并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，另一个负责生成用于渲染场景的颜色。实验结果表明，在反映人类感知的质量指标上，BioNeRF在两个数据集（真实世界图像与合成数据）中均超越了当前最优结果。