Multiview Stereo with Cascaded Epipolar RAFT

Zeyu Ma, Zachary Teed, Jia Deng

Abstract: 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 CERMVS (Cascaded Epipolar RAFT Multiview Stereo), a new approach based on the RAFT (Recurrent All-Pairs Field Transforms) architecture developed for optical flow. CERMVS 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 published 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