Same rough usecase but not the same output. The SIFT stereoscopy approach would build a 3D model while this learns a field. Meaning that this deals properly wi

Same rough usecase but not the same output. The SIFT stereoscopy approach would build a 3D model while this learns a field. Meaning that this deals properly with things like reflection and transparency. Also it is resistant to partial occlusion that appears randomly in the training data (people in the photographs). The huge downside is that you have to train a neural network every time you want a model: that is very slow, ressource intensive and fragile.