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Automatic real-time 2D-to-3D video conversion

University of British Columbia

The generation of three-dimensional (3D) videos from monoscopic two-dimensional (2D) videos has received a lot of attention in the last few years. Although the concept of 3D has existed for a long time, the research on converting from 2D-to-3D in real-time is still on going. Current conversion techniques are based on generating an estimated depth map for each frame from different depth cues, and then using Depth Image Based Rendering (DIBR) to synthesize additional views. Efficient interactive techniques have been developed in which multiple depth factors (monocular depth cues) are utilized to estimate the depth map using machine-learning algorithms. The challenge with such methods is that they cannot be used for real-time conversion. We address this problem by proposing an effective scheme that generates high quality depth maps for indoor and outdoor scenes in real-time. In our work,we classify the 2D videos into indoor or outdoor categories using machine-learning-based scene classification. Subsequently, we estimate the initial depth mapsfor each video frame using different depth cues based onthe classification results. Then, we fuse these depth maps and the final depth map is evaluated in two steps. First, depth values are estimated at edges. Then, these depth values are propagated to the rest of the image using an edge-aware interpolation method. Performance evaluations show that our method outperforms the existing state-of-the-art 2D-to3D conversion methods.

Text

2016-08-03

Attribution-NonCommercial-NoDerivatives 4.0 International

http://hdl.handle.net/2429/58634

Electrical and Computer Engineering

University of British Columbia

UBCV

http://creativecommons.org/licenses/by-nc-nd/4.0/