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http://dx.doi.org/10.5392/JKCA.2018.18.11.447

Depth Map Estimation Model Using 3D Feature Volume  

Shin, Soo-Yeon (충북대학교)
Kim, Dong-Myung (충북대학교)
Suh, Jae-Won (충북대학교)
Publication Information
The Journal of the Korea Contents Association / v.18, no.11, 2018 , pp. 447-454 More about this Journal
Abstract
This paper proposes a depth image generation algorithm of stereo images using a deep learning model composed of a CNN (convolutional neural network). The proposed algorithm consists of a feature extraction unit which extracts the main features of each parallax image and a depth learning unit which learns the parallax information using extracted features. First, the feature extraction unit extracts a feature map for each parallax image through the Xception module and the ASPP(Atrous spatial pyramid pooling) module, which are composed of 2D CNN layers. Then, the feature map for each parallax is accumulated in 3D form according to the time difference and the depth image is estimated after passing through the depth learning unit for learning the depth estimation weight through 3D CNN. The proposed algorithm estimates the depth of object region more accurately than other algorithms.
Keywords
Depth Map; Convolutional Neural Network; Deep Learning; Stereo Image;
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