monodepth2
MonoDepth2 is a reference PyTorch implementation for self-supervised monocular depth estimation, based on the ICCV 2019 paper Digging into Self-Supervised Monocular Depth Prediction by Godard et al. The software enables training and testing models to predict depth maps from single images without requiring ground truth depth data during training. It achieves this through a novel self-supervised approach that leverages stereo pairs or video sequences to minimize photometric reprojection error. The codebase supports various pre-trained models trained with mono-only, stereo-only, or combined mono-stereo modalities at resolutions of 640x192 and 1024x320. Key capabilities include predicting scaled disparity or metric depth for single input images using provided command-line scripts. It supports both training new models and evaluating existing ones on standard benchmarks like KITTI. The system requires PyTorch, CUDA, and OpenCV, with specific recommendations for environment setup using Anaconda to manage dependencie