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.
Multi-view stereo reconstruction using CNN algorithms have been recently proposed REF .
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