Efficient computation of visual saliency regions has been a research problem in the recent past, but in the case of 3D content no definite solutions exist. This paper presents a computational method to determine saliency regions in 3D video, based on fusion of three feature maps containing perceptually relevant information from spatial, temporal and depth dimensions. The proposed method follows a bottom-up approach to predict the 3D regions where observers tend to hold their gaze for longer periods. Fusion of the feature maps is combined with a center-bias weighting function to determine 3D visual saliency map. For validation and performance evaluation, a publicly available database of 3D video sequences and corresponding fixation density maps was used as ground-truth. The experimental results show that the proposed method achieves better performance than other state-of-art models.