  We address multiview stereo (MVS), an important 3D vision task that
reconstructs a 3D model such as a dense point cloud from multiple calibrated
images. We propose CER-MVS (Cascaded Epipolar RAFT Multiview Stereo), a new
approach based on the RAFT (Recurrent All-Pairs Field Transforms) architecture
developed for optical flow. CER-MVS introduces five new changes to RAFT:
epipolar cost volumes, cost volume cascading, multiview fusion of cost volumes,
dynamic supervision, and multiresolution fusion of depth maps. CER-MVS is
significantly different from prior work in multiview stereo. Unlike prior work,
which operates by updating a 3D cost volume, CER-MVS operates by updating a
disparity field. Furthermore, we propose an adaptive thresholding method to
balance the completeness and accuracy of the reconstructed point clouds.
Experiments show that our approach achieves competitive performance on DTU (the
second best among known results) and state-of-the-art performance on the
Tanks-and-Temples benchmark (both the intermediate and advanced set). Code is
available at https://github.com/princeton-vl/CER-MVS
