Sreenivas Kothandaraman

Auto convergence for stereoscopic 3D cameras

Viewing comfort is an important concern for 3-D capable consumer electronics such as 3-D cameras and TVs. Consumer generated content is typically viewed at a close distance which makes the vergence-accommodation conflict particularly pronounced, causing discomfort and eye fatigue. In this paper, we present a Stereo Auto Convergence (SAC) algorithm for consumer 3-D cameras that reduces the vergence-accommodation conflict on the 3-D display by adjusting the depth of the scene automatically. Our algorithm processes stereo video in realtime and shifts each stereo frame horizontally by an appropriate amount to converge on the chosen object in that frame. The algorithm starts by estimating disparities between the left and right image pairs using correlations of the vertical projections of the image data. The estimated disparities are then analyzed by the algorithm to select a point of convergence. The current and target disparities of the chosen convergence point determines how much horizontal shift is needed. A disparity safety check is then performed to determine whether or not the maximum and minimum disparity limits would be exceeded after auto convergence. If the limits would be exceeded, further adjustments are made to satisfy the safety limits. Finally, desired convergence is achieved by shifting the left and the right frames accordingly. Our algorithm runs real-time at 30 fps on a TI OMAP4 processor. It is tested using an OMAP4 embedded prototype stereo 3-D camera. It significantly improves 3-D viewing comfort.