Introduction to Monodepth2
Monodepth2 is a reference implementation in PyTorch for training and testing models that estimate depth from a single image. This approach, called "self-supervised monocular depth prediction," was detailed in a paper presented at ICCV 2019 by Clément Godard, Oisin Mac Aodha, Michael Firman, and Gabriel J. Brostow. This project leverages groundbreaking techniques to predict depth information from a single camera view, which has traditionally been challenging compared to stereo vision systems.
Project Overview
Monodepth2 is designed primarily for non-commercial use, and the team encourages citation of their work if found useful in academic research. This project is particularly notable for requiring no ground-truth depth data during training, instead relying on stereo image pairs for supervision, thereby achieving robust depth estimation.
Setup
For those interested in exploring Monodepth2, setting it up requires a few dependencies. The recommended environment is a fresh Anaconda setup where users can easily install PyTorch, TensorBoardX, and OpenCV (for evaluation purposes). Users might encounter compatibility issues depending on their Python version, especially with Python 3.7 and certain versions of OpenCV. Creating a dedicated virtual environment can help circumvent these issues.
Depth Prediction for Single Images
Monodepth2 offers a straightforward way to predict depth from images. By running a simple script, users can predict a scaled disparity map from a given image. The project provides several pre-trained models, which differ based on their training modality (monocular, stereo, or both) and resolution. These pre-trained models save time and computational resources for users when trying to estimate depth maps.
Training with KITTI Dataset
Monodepth2 primarily uses the KITTI dataset, a well-known dataset in the field of computer vision, particularly for autonomous driving research. Users can download and prepare this dataset to train their models. There are several predefined dataset splits for training, testing, and validation, and users can also apply the project to their custom datasets by extending the provided data loader classes.
Training Your Model
Training is flexible, allowing for various configurations through command-line options. Users can choose different training setups, such as monocular training, stereo training, or a combination of both. Finetuning pre-trained models is also supported, facilitating enhancements to existing models based on new data or additional epochs.
Evaluation
The project includes comprehensive evaluation tools. Users can test their trained models against different KITTI benchmark splits and obtain visual and quantitative results. For stereo models, specific adjustments during evaluation are necessary due to baseline differences with the KITTI rig.
Precomputed Results and Evaluation
For those wanting to evaluate without training, Monodepth2 provides precomputed disparity maps. These results can help verify the project’s outputs or serve as a baseline for further innovations.
Licensing
Owned by Niantic, Inc., Monodepth2 comes with a license suitable for research purposes, ensuring that the project's implementations can be utilized under specific terms.
In summary, Monodepth2 stands out as an accessible and efficient method for depth estimation, equipped with robust tools for both training and evaluation while eliminating the dependency on expensive depth sensing hardware.