Details

Autor(en) / Beteiligte

• Chang, Jia-Ren
• Chen, Yong-Sheng

Titel

Pyramid Stereo Matching Network

Ist Teil von

• 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2018, p.5410-5418

Ort / Verlag

IEEE

Erscheinungsjahr

2018

Quelle

IEEE Electronic Library Online

Beschreibungen/Notizen

• Recent work has shown that depth estimation from a stereo pair of images can be formulated as a supervised learning task to be resolved with convolutional neural networks (CNNs). However, current architectures rely on patch-based Siamese networks, lacking the means to exploit context information for finding correspondence in ill-posed regions. To tackle this problem, we propose PSMNet, a pyramid stereo matching network consisting of two main modules: spatial pyramid pooling and 3D CNN. The spatial pyramid pooling module takes advantage of the capacity of global context information by aggregating context in different scales and locations to form a cost volume. The 3D CNN learns to regularize cost volume using stacked multiple hourglass networks in conjunction with intermediate supervision. The proposed approach was evaluated on several benchmark datasets. Our method ranked first in the KITTI 2012 and 2015 leaderboards before March 18, 2018. The codes of PSMNet are available at: https://github.com/JiaRenChang/PSMNet.