1 research outputs found
Parallax Motion Effect Generation Through Instance Segmentation And Depth Estimation
Stereo vision is a growing topic in computer vision due to the innumerable
opportunities and applications this technology offers for the development of
modern solutions, such as virtual and augmented reality applications. To
enhance the user's experience in three-dimensional virtual environments, the
motion parallax estimation is a promising technique to achieve this objective.
In this paper, we propose an algorithm for generating parallax motion effects
from a single image, taking advantage of state-of-the-art instance segmentation
and depth estimation approaches. This work also presents a comparison against
such algorithms to investigate the trade-off between efficiency and quality of
the parallax motion effects, taking into consideration a multi-task learning
network capable of estimating instance segmentation and depth estimation at
once. Experimental results and visual quality assessment indicate that the
PyD-Net network (depth estimation) combined with Mask R-CNN or FBNet networks
(instance segmentation) can produce parallax motion effects with good visual
quality.Comment: 2020 IEEE International Conference on Image Processing (ICIP), Abu
Dhabi, United Arab Emirate