Semantically-Agnostic Unsupervised Monocular Depth Learning in Dynamic Scenes

We present a method for jointly training the estimation of depth, egomotion, and a dense 3D translation field of objects, suitable for dynamic scenes containing multiple moving objects. Monocular photometric consistency is the sole source of supervision. We show that this apparently heavily-underdetermined problem can be regularized by imposing the following prior knowledge about 3D translation fields: They are sparse, since most of the scene is static, and they tend to be constant through rigid moving objects. We show that this regularization alone is sufficient to train monocular depth prediction models that exceed the accuracy achieved in prior work, including methods that require semantic input.