Multi-View Stereo (MVS) is a core task in 3D computer vision. With the surge
of novel deep learning methods, learned MVS has surpassed the accuracy of
classical approaches, but still relies on building a memory intensive dense
cost volume. Novel View Synthesis (NVS) is a parallel line of research and has
recently seen an increase in popularity with Neural Radiance Field (NeRF)
models, which optimize a per scene radiance field. However, NeRF methods do not
generalize to novel scenes and are slow to train and test. We propose to bridge
the gap between these two methodologies with a novel network that can recover
3D scene geometry as a distance function, together with high-resolution color
images. Our method uses only a sparse set of images as input and can generalize
well to novel scenes. Additionally, we propose a coarse-to-fine sphere tracing
approach in order to significantly increase speed. We show on various datasets
that our method reaches comparable accuracy to per-scene optimized methods
while being able to generalize and running significantly faster. We provide the
source code at this https URL

本文提出了一种新型网络，利用少量稀疏图像输入，能够恢复三维场景几何信息和高分辨率彩色图像，并通过粗略到精细的球形追踪技术可以大幅提高速度，方法在多个数据集中都取得了可比较的精度。