Official PyTorch implementation of self-supervised monocular depth estimation methods invented by the ML Team at Toyota Research Institute (TRI), in particular for PackNet: 3D Packing for Self-Supervised Monocular Depth Estimation (CVPR 2020 oral), Vitor Guizilini, Rares Ambrus, Sudeep Pillai, Allan Raventos and Adrien Gaidon. Although self-supervised (i.e. trained only on monocular videos), PackNet outperforms other self, semi, and fully supervised methods. Furthermore, it gets better with input resolution and number of parameters, generalizes better, and can run in real-time (with TensorRT). See References for more info on our models. This is also the official implementation of Neural Ray Surfaces for Self-Supervised Learning of Depth and Ego-motion (3DV 2020 oral), Igor Vasiljevic, Vitor Guizilini, Rares Ambrus, Sudeep Pillai, Wolfram Burgard, Greg Shakhnarovich and Adrien Gaidon. Neural Ray Surfaces (NRS) generalize self-supervised depth and pose estimation beyond the pinhole model to all central cameras, allowing the learning of meaningful depth and pose on non-pinhole cameras such as fisheye and catadioptric.