NeLF: Practical Novel View Synthesis with Neural Light Field

In this paper, we present a practical and robust deep learning solution for the novel view synthesis of complex scenes. In our approach, a continuous scene is represented as a light field, i.e., a set of rays, each of which has a corresponding color. We adopt a 4D parameterization of the light field. We then formulate the light field as a 4D function that maps 4D coordinates to corresponding color values. We train a deep fully connected network to optimize this function. Then, the scene-specific model is used to synthesize novel views. Previous light field approaches usually require dense view sampling to reliably render high-quality novel views. Our method can render novel views by sampling rays and querying the color for each ray from the network directly; thus enabling fast light field rendering with a very sparse set of input images. Our method achieves state-of-the-art novel view synthesis results while maintaining an interactive frame rate.