Abstract: In general, a “Stereoscopic Video Converter” (SVC) provides various techniques for automatically converting arbitrary 2D video sequences into perceptually plausible stereoscopic or “3D” versions while optionally generating dense depth maps for every frame of the video sequence. In particular, the automated 2D-to-3D conversion process first automatically estimates scene depth for each frame of an input video sequence via a label transfer process that matches features extracted from those frames with features from a database of images and videos having known ground truth depths. The estimated depth distributions for all image frames of the input video sequence are then used by the SVC for automatically generating a “right view” of a corresponding stereoscopic image for each frame (assuming that each original input frame represents the “left view” of the stereoscopic image).

Abstract: In general, a “Stereoscopic Video Converter” (SVC) provides various techniques for automatically converting arbitrary 2D video sequences into perceptually plausible stereoscopic or “3D” versions while optionally generating dense depth maps for every frame of the video sequence. In particular, the automated 2D-to-3D conversion process first automatically estimates scene depth for each frame of an input video sequence via a label transfer process that matches features extracted from those frames with features from a database of images and videos having known ground truth depths. The estimated depth distributions for all image frames of the input video sequence are then used by the SVC for automatically generating a “right view” of a corresponding stereoscopic image for each frame (assuming that each original input frame represents the “left view” of the stereoscopic image).