  In recent years, novel view synthesis has gained popularity in generating
high-fidelity images. While demonstrating superior performance in the task of
synthesizing novel views, the majority of these methods are still based on the
conventional multi-layer perceptron for scene embedding. Furthermore, light
field models suffer from geometric blurring during pixel rendering, while
radiance field-based volume rendering methods have multiple solutions for a
certain target of density distribution integration. To address these issues, we
introduce the Convolutional Neural Radiance Fields to model the derivatives of
radiance along rays. Based on 1D convolutional operations, our proposed method
effectively extracts potential ray representations through a structured neural
network architecture. Besides, with the proposed ray modeling, a proposed
recurrent module is employed to solve geometric ambiguity in the fully neural
rendering process. Extensive experiments demonstrate the promising results of
our proposed model compared with existing state-of-the-art methods.
