Automatic depth estimation from single 2D image via transfer learning approach

Authors: Shoukat, M.A., Sargano, A.B., Habib, Z. and You, L.

Journal: Proceedings - 2018 2nd European Conference on Electrical Engineering and Computer Science, EECS 2018

Pages: 589-594

ISBN: 9781728119298

DOI: 10.1109/EECS.2018.00114

Abstract:

Nowadays, depth estimation from a single 2D image is a prominent task due to its numerous applications such as 2D to 3D image/video conversion, robot vision, and self-driving cars. This research proposes an automatic novel technique for the depth estimation of single 2D images via transfer learning of pre-trained deep learning model. This is a challenging problem, as a single 2D image does not carry any cues regarding depth. To tackle this, the pool of available images is exploited for which the depth is known. By following the hypothesis that the color images having similar semantics are most probably to have similar depth. Along these lines, the depth of the input image is predicted through corresponding depth maps of semantically similar images available in the dataset, fetched by high-level features of pre-trained deep learning model followed by a classifier (i.e., K-Nearest Neighbor). Afterward, a Cross Bilateral filter is applied for the removal of fallacious depth variations in the depth map. To prove the quality of the presented approach, different experiments have been conducted on two publicly available benchmark datasets, NYU (v2) and Make3D. The results indicate that the proposed approach outperforms state of the art methods.

http://eprints.bournemouth.ac.uk/33209/

Source: Scopus

Automatic Depth Estimation from Single 2D Image via Transfer Learning Approach

Authors: Shoukat, M.A., Sargano, A.B., Habib, Z., You, L. and IEEE

Journal: 2018 2ND EUROPEAN CONFERENCE ON ELECTRICAL ENGINEERING AND COMPUTER SCIENCE (EECS 2018)

Pages: 589-594

DOI: 10.1109/EECS.2018.00114

http://eprints.bournemouth.ac.uk/33209/

Source: Web of Science (Lite)

Automatic depth estimation from single 2D image via transfer learning approach

Authors: Shoukat, M.A., Sargano, A.B., Habib, Z. and You, L.

Conference: 2nd European Conference on Electrical Engineering and Computer Science (EECS)

Pages: 589-594

ISBN: 9781728119298

Abstract:

Nowadays, depth estimation from a single 2D image is a prominent task due to its numerous applications such as 2D to 3D image/video conversion, robot vision, and self-driving cars. This research proposes an automatic novel technique for the depth estimation of single 2D images via transfer learning of pre-trained deep learning model. This is a challenging problem, as a single 2D image does not carry any cues regarding depth. To tackle this, the pool of available images is exploited for which the depth is known. By following the hypothesis that the color images having similar semantics are most probably to have similar depth. Along these lines, the depth of the input image is predicted through corresponding depth maps of semantically similar images available in the dataset, fetched by high-level features of pre-trained deep learning model followed by a classifier (i.e., K-Nearest Neighbor). Afterward, a Cross Bilateral filter is applied for the removal of fallacious depth variations in the depth map. To prove the quality of the presented approach, different experiments have been conducted on two publicly available benchmark datasets, NYU (v2) and Make3D. The results indicate that the proposed approach outperforms state of the art methods.

http://eprints.bournemouth.ac.uk/33209/

Source: BURO EPrints