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Synthesizing images with out-of-plane transformations using stereo images

Using a pair of stereo cameras, we can acquire the depth information, and thus synthesize images with 3D transformations. However, the resulting images often look uncanny because of data noises. In this project, we attempt to repair the generated stereo images. One way to address this problem is via unsupervised feature learning that could be achieved using linear autoencoders, also known as Reconstruction Independent Component Analysis (RICA). This algorithm has been shown to outperform other algorithms by penalizing lack of diversity among features. We improve the algorithm by adding an extension layer, and compare its performance with another image inpainting technique based on fast marching method. Our research shows that the inpainting method holds good promise.