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.

翻译：近年来，新视角合成在生成高保真图像方面受到广泛关注。尽管现有方法在合成新视角任务中展现出优异性能，但多数仍基于传统多层感知机进行场景嵌入。此外，光场模型在像素渲染过程中存在几何模糊问题，而基于辐射场的体渲染方法在密度分布积分目标上存在多解性。针对上述问题，我们提出卷积神经辐射场以建模沿射线的辐射导数。基于一维卷积操作，所提方法通过结构化神经网络架构有效提取潜在射线表征。同时，结合所提射线建模方法，采用循环模块解决全神经渲染过程中的几何歧义问题。大量实验表明，与现有最先进方法相比，本模型取得了显著成果。