一种联合采样的神经网络光场

相比传统的光场绘制技术,神经网络光场(NeRF)方法可使用神经网络拟合场景的光线采样,将隐式编码输入图片的光场,合成新视图. 针对NeRF方法训练时间长,绘制视图慢的问题,提出了一种基于联合采样的NeRF方法,通过使粗糙网络和细腻网络共享均匀采样结果的方法,减少了不必要的光线采样,从而加快了网络训练和视图合成的速度. 实验结果表明,在取得近似视图合成质量的情况下,与NeRF方法相比,所提方法的训练时间减少了20%,视图合成的效率提高了25%.

Neural Radiance Field by Joint Sampling

Compared with the traditional light field, the neural reflectance field (NeRF) method uses the neural network to fit the light sampling of scenes, which implicitly encodes the light field from input images to render novel view. However, NeRF method requires long training time and has slow rendering speed. To solve this problem, a joint sampling-based NeRF is proposed, which can make the coarse network and fine network share uniform sampling results, thereby accelerating the network training and view synthesis by reducing unnecessary light sampling. The experiments demonstrate that, in the case of the similar view synthesis quality, compared with the baseline method, the proposed method can reduce the training time by 20% and improve the view synthesis efficiency by 25%.