콘텐츠 본문
논문 해외 국제전문학술지(SCI급) A Novel Method for Estimating Monocular Depth Using Cycle GAN and Segmentation
- 학술지 구분 국제전문학술지(SCI급)
- 게재년월 2020-04
- 저자명 Dong-hoon Kwak, Seung-ho Lee
- 학술지명 SENSORS
- 발행국가 해외
- 논문언어 외국어
- 전체저자수 2
- 논문 다운로드 링크(외부) https://doi.org/10.3390/s20092567
- 연구분야 공학 > 전자/정보통신공학
논문 초록 (Abstract)
Modern image processing techniques use three-dimensional (3D) images, which contain spatial information such as depth and scale, in addition to visual information. These images are indispensable in virtual reality, augmented reality (AR), and autonomous driving applications. We propose a novel method to estimate monocular depth using a cycle generative adversarial network (GAN) and segmentation. In this paper, we propose a method for estimating depth information by combining segmentation. It uses three processes: segmentation and depth estimation, adversarial loss calculations, and cycle consistency loss calculations. The cycle consistency loss calculation process evaluates the similarity of two images when they are restored to their original forms after being estimated separately from two adversarial losses. To evaluate the objective reliability of the proposed method, we compared our proposed method with other monocular depth estimation (MDE) methods using the NYU Depth Dataset V2. Our results show that the benchmark value for our proposed method is better than other methods. Therefore, we demonstrated that our proposed method is more efficient in determining depth estimation.
