This article proposes a three-dimensional light field reconstruction method based on neural radiation field （NeRF） called Infrared NeRF for low resolution thermal infrared scenes. Based on the characteristics of low resolution thermal infrared imaging， various optimizations have been carried out to improve the speed and accuracy of thermal infrared 3D reconstruction. Firstly， inspired by Boltzmann"s law of thermal radiation， distance was incorporated into the NeRF model for the first time， resulting in a nonlinear propagation of a single ray and a more accurate description of the physical property that infrared radiation intensity decreases with increasing distance. Secondly， in terms of improving inference speed， based on the phenomenon of high and low frequency distribution of foreground and background in infrared images， a multi ray non-uniform light synthesis strategy is proposed to make the model pay more attention to foreground objects in the scene， reduce the distribution of light in the background， and significantly reduce training time without reducing accuracy. In addition， compared to visible light scenes， infrared images only have a single channel， so fewer network parameters are required. Experiments using the same training data and data filtering method showed that compared to the original NeRF， the improved network achieved an average improvement of 13.8% and 4.62% in PSNR and SSIM， respectively， while an average decrease of 46% in LPIPS. And thanks to the optimization of network layers and data filtering methods， training only takes about 25% of the original method"s time to achieve convergence. Finally， for scenes with weak backgrounds， this article improves the inference speed of the model by 4-6 times compared to the original NeRF by limiting the query interval of the model.