%0 Generic %1 huang2018deepmvs %A Huang, Po-Han %A Matzen, Kevin %A Kopf, Johannes %A Ahuja, Narendra %A Huang, Jia-Bin %D 2018 %K 2018 arxiv deep-learning multi-view paper reconstruction stereo %T DeepMVS: Learning Multi-view Stereopsis %U http://arxiv.org/abs/1804.00650 %X 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.

@misc{huang2018deepmvs, 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.}, added-at = {2018-04-05T17:52:30.000+0200}, author = {Huang, Po-Han and Matzen, Kevin and Kopf, Johannes and Ahuja, Narendra and Huang, Jia-Bin}, biburl = {https://www.bibsonomy.org/bibtex/2db3906402a7e1e678fced0678a03d296/achakraborty}, description = {[1804.00650] DeepMVS: Learning Multi-view Stereopsis}, interhash = {d2ec923ffbb15ad69132f4feac8ca556}, intrahash = {db3906402a7e1e678fced0678a03d296}, keywords = {2018 arxiv deep-learning multi-view paper reconstruction stereo}, note = {cite arxiv:1804.00650Comment: CVPR 2018. Project page: https://phuang17.github.io/DeepMVS/ Code: https://github.com/phuang17/DeepMVS}, timestamp = {2018-04-05T17:52:30.000+0200}, title = {DeepMVS: Learning Multi-view Stereopsis}, url = {http://arxiv.org/abs/1804.00650}, year = 2018 }