redicting a convincing depth map from a monocular RGB image is a daunting task in the field of computer vision. An efficient detail preserving depth prediction algorithm on the basis of multi-scale deep network and gradient network is presented in this paper. Specifically, the proposed method leverage the designed multi-scale deep network to obtain the global depth image from training datasets. Moreover, a depth gradient generation strategy on the basis of gradient network, which permits us to obtain the local depth detail information of the scene is developed. In the end, the reliable depth map with better details could be reconstructed via merging the depth gradient and depth information on the basis of the optimization algorithm. Experimental results evaluated from the qualitative and quantitative perspective on the Make 3D and NYUv2 datasets indicate that the designed depth prediction scheme is superior to several depth estimation approaches, and can reconstruct plausible depths
Access to the requested content is limited to institutions that have purchased or subscribe to SPIE eBooks.
You are receiving this notice because your organization may not have SPIE eBooks access.*
*Shibboleth/Open Athens users─please
sign in
to access your institution's subscriptions.
To obtain this item, you may purchase the complete book in print or electronic format on
SPIE.org.
INSTITUTIONAL Select your institution to access the SPIE Digital Library.
PERSONAL Sign in with your SPIE account to access your personal subscriptions or to use specific features such as save to my library, sign up for alerts, save searches, etc.