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Found 3 papers, 1 papers with code
Counterfactual Depth from a Single RGB Image
We describe a method that predicts, from a single RGB image, a depth map that describes the scene when a masked object is removed - we call this "counterfactual depth" that models hidden scene geometry together with the observations.
SafetyNet: Detecting and Rejecting Adversarial Examples Robustly
SceneProof applies to images captured with depth maps (RGBD images) and checks if a pair of image and depth map is consistent.
Authoring image decompositions with generative models
Instead, we can learn to decompose an image into layers that are "like this" by authoring generative models for each layer using proxy examples that capture the Platonic ideal (Mondrian images for albedo; rendered 3D primitives for shading; material swatches for shading detail).
