DeepMVS: Learning Multi-View Stereopsis
Po-Han Huang, Kevin Matzen, Johannes Kopf, Narendra Ahuja, Jia-Bin Huang
Abstract
We present DeepMVS, a deep convolutional neural network (ConvNet) for multi-view stereo reconstruction. Taking an arbitrary number of posed images as input, we first produce a set of plane-sweep volumes and use the proposed DeepMVS network to predict high-quality disparity maps. The key contributions that enable these results are (1) supervised pretraining on a photorealistic synthetic dataset, (2) an effective method for aggregating information across a set of unordered images, and (3) integrating multi-layer feature activations from the pre-trained VGG-19 network. We validate the efficacy of DeepMVS using the ETH3D Benchmark. Our results show that DeepMVS compares favorably against state-of-the-art conventional MVS algorithms and other ConvNet based methods, particularly for near-textureless regions and thin structures.
BibTeX
@inproceedings{cvpr2018_deepmvslearningm,
title = {DeepMVS: Learning Multi-View Stereopsis},
author = {Po-Han Huang and Kevin Matzen and Johannes Kopf and Narendra Ahuja and Jia-Bin Huang},
booktitle = {CVPR 2018},
year = {2018}
}