Disparity estimation is a popular topic in computer vision and has drawn increasing attention in recent years. In this3 article, we propose a new multi-stage network for the purpose4 of two to three-dimensional video conversion that contains5 two training stages: an initial disparity estimation as the first training stage and depth-image-based rendering (DIBR) as an extra component to form the second training stage. In the first training stage, we propose a revised end-to-end feature pyramid stereo network, in which the original non-pyramid structure is replaced by a bottom-up convolutional neural network pyramid for disparity regression. It utilizes the spatial information by concatenating different scale features to boost the performance on boundary consistency. Mirror connections between feature extraction and disparity regression on the corresponding layers are also added to improve the quality of the results. In the second stage, we propose an improved disocclusion filling technique in the DIBR branch and connect the non-neural-network method to the disparity estimation network. This two-stage training strategy can work effectively to generate the improved disparity estimation for two to three-dimensional video conversion. Exten21 sive experiments are conducted and some selected state-of-the art algorithms are compared with our proposed approach on the popular KITTI2015 and Scene Flow datasets. The results demonstrate that our estimated disparity map can generate high quality 3D images.