The use of filters, in convolutions stretched horizontally, is since the main distortions are horizontal displacements of some objects. Using information about the geometric position of the pixel (usually implemented as a CoordConv layer, I just added 2 input channels using NumPy, which is equivalent) Usage of Skip-connections - the frame for the right eye should be similar to the frame for the left eye

The output of the neural network has been renormalized for faster further learning. Using a pre-trained network to determine the distance to objects (excellent results were obtained here).The 4 significant features are important for the model:

Proposed an end-to-end model without adding knowledge of the geometry of the world and the position of the eyes.

The task of determining the distance to an object (depth) with one eye is impossible due to a lack of data. Ideally, generating (rendering) an image for the eye requires knowledge of the spatial position of all visible objects. Several problems do not lend themselves well to software algorithmic solutions. Until recently, this task did not lend itself well to automation due to the lack of an unambiguous solution when restoring a stereoscopic image from a monocular one. For high-quality conversion of old films into 3D and showing them in the cinema, manual work of a large number of artists is required to mark the scene and manually redraw most of the frames for the other eye. However, to create a stereoscopic film, 2 video cameras are used, located at a distance of about 6.5 cm, filming 2 videos simultaneously. With the advent of cheap virtual reality glasses, it can be tempting to watch your favorite movies in 3D. However, due to the requirements for playback equipment and the availability of special glasses for viewing, stereoscopic films are still not the standard for filming. I found that the technology for creating stereoscopic 3D films is about 100 years old - more than the history of recording combined audio and video tracks.

Auto 2d to 3d video converter