a) Input (b) Deep multi-view stereo (c) Sample reconstruction results Figure 1. (a) Our network takes a set of images with known camera poses and calibration as input; ( b) we produce a set of plane-sweep volumes for a reference view and feed these into a convolutional neural network that predicts a disparity map; (c) our network produces high-quality disparity maps even for challenging cases containing poorly textured regions and thin structures. 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 neartextureless regions and thin structures.
DeepMVS REF introduced image patch matching networks to estimate depth maps, which enables an arbitrary number of images to be taken as input.
DeepMVS: Learning Multi-view Stereopsis
2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition
2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition
Computer Science