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.

提出了一种针对多视点立体相机图像的第一种通用视角合成方法，该方法引入了立体匹配以实现高质量的几何重建。该方法包括三个关键组件：立体特征提取器、深度引导的平面扫描和立体深度损失，并提出了第一个多视点数据集 StereoNVS，实验证明该方法优于以前的方法。