  Novel view synthesis (NVS) is a challenging task requiring systems to
generate photorealistic images of scenes from new viewpoints, where both
quality and speed are important for applications. Previous image-based
rendering (IBR) methods are fast, but have poor quality when input views are
sparse. Recent Neural Radiance Fields (NeRF) and generalizable variants give
impressive results but are not real-time. In our paper, we propose a
generalizable NVS method with sparse inputs, called FWD, which gives
high-quality synthesis in real-time. With explicit depth and differentiable
rendering, it achieves competitive results to the SOTA methods with 130-1000x
speedup and better perceptual quality. If available, we can seamlessly
integrate sensor depth during either training or inference to improve image
quality while retaining real-time speed. With the growing prevalence of depths
sensors, we hope that methods making use of depth will become increasingly
useful.
