Eye For An Eye: A Deep-Learning and Analytical Method to Spatializing Stereoscopic Images

This project studies stereopsis-inspired image transformation: given a scene from one viewpoint, generate the corresponding image from a slightly offset viewpoint. The work compares a Pix2Pix-only image translation model, a hybrid method that combines monocular depth estimation with analytical pixel shifting before Pix2Pix, and a final variant that adds interpolation to repair blank regions introduced by the shift.

The interpolation-enhanced hybrid model performs best in the article's evaluation, reaching SSIM 0.81 and PSNR 20.6 dB on the held-out test set. The result supports a practical design principle: analytical depth cues can make the learned perspective transform easier, while the generator handles visual reconstruction and local realism.