Computational algorithms for increased control of depth-viewing volume for stereo three-dimensional graphic displays

Three-dimensional pictorial displays incorporating depth cues by means of stereopsis offer a potential means of presenting information in a natural way to enhance situational awareness and improve operator performance. Conventional computational techniques rely on asymptotic projection transformations and symmetric clipping to produce the stereo display. Implementation of two new computational techniques, as asymmetric clipping algorithm and piecewise linear projection transformation, provides the display designer with more control and better utilization of the effective depth-viewing volume to allow full exploitation of stereopsis cuing. Asymmetric clipping increases the perceived field of view (FOV) for the stereopsis region. The total horizontal FOV provided by the asymmetric clipping algorithm is greater throughout the scene viewing envelope than that of the symmetric algorithm. The new piecewise linear projection transformation allows the designer to creatively partition the depth-viewing volume, with freedom to place depth cuing at the various scene distances at which emphasis is desired

Unsupervised Learning of Depth and Ego-Motion from Video

We present an unsupervised learning framework for the task of monocular depth and camera motion estimation from unstructured video sequences. We achieve this by simultaneously training depth and camera pose estimation networks using the task of view synthesis as the supervisory signal. The networks are thus coupled via the view synthesis objective during training, but can be applied independently at test time. Empirical evaluation on the KITTI dataset demonstrates the effectiveness of our approach: 1) monocular depth performing comparably with supervised methods that use either ground-truth pose or depth for training, and 2) pose estimation performing favorably with established SLAM systems under comparable input settings.Comment: Accepted to CVPR 2017. Project webpage: https://people.eecs.berkeley.edu/~tinghuiz/projects/SfMLearner