CodeSLAM
CodeSLAM
CodeSLAM represents the 3D geometry of a scene using the latent space of a variational autoencoder. The depth thus becomes a function of the RGB image and the unknown code, $D = G_\theta(I,c)$. During training time, the weights of the network $G_\theta$ are learnt by training the generator and encoder using a standard autoencoding task. At test time the code $c$ and the pose of the images is found by optimizing the reprojection error over multiple images.
CodeSLAM - Learning a Compact, Optimisable Representation for Dense Visual SLAM
https://paperswithcode.com/paper/codeslam-learning-a-compact-optimisable
CodeSLAM - Learning a Compact, Optimisable Representation for Dense Visual SLAM