A Learning-Based Method for Conditioning Neural Light Fields From Limited Inputs

This is the authors' abstract. We don't add key points for this paper.

This work proposes a novel approach for few-shot novel view synthesis based on a neural light field representation. Our method leverages an implicit neural network to map each ray directly to its target pixel's color based on a given target camera pose. This implicit neural network is conditioned on local ray features generated by coarse volumetric rendering from an explicit feature volume. The volume is built from the input images using convolutional neural networks (CNNs). Conditioning the network with local ray features enables us to generalize well to novel views of both seen and unseen scenes from sparse inputs. Moreover, using a light field network helps to reduce the computational cost while still allowing the network to learn complex relationships between input views and target views. Our approach achieves competitive performance across different datasets captured by the sensor camera, including LLFF data, synthetic neural radiance field (NeRF) data, and real multiview stereo (DTU) data, while offering much faster rendering speed than baselines.