  In this paper, we propose the first generalizable view synthesis approach
that specifically targets multi-view stereo-camera images. Since recent stereo
matching has demonstrated accurate geometry prediction, we introduce stereo
matching into novel-view synthesis for high-quality geometry reconstruction. To
this end, this paper proposes a novel framework, dubbed StereoNeRF, which
integrates stereo matching into a NeRF-based generalizable view synthesis
approach. StereoNeRF is equipped with three key components to effectively
exploit stereo matching in novel-view synthesis: a stereo feature extractor, a
depth-guided plane-sweeping, and a stereo depth loss. Moreover, we propose the
StereoNVS dataset, the first multi-view dataset of stereo-camera images,
encompassing a wide variety of both real and synthetic scenes. Our experimental
results demonstrate that StereoNeRF surpasses previous approaches in
generalizable view synthesis.
