The ever-evolving field of computer vision continuously presents new methods for achieving accurate and efficient results. One such approach is encapsulated in the CVPR 2021 paper titled Deep Two-View Structure-from-Motion Revisited. This article will guide you on how to use the provided repository effectively, from setting up your environment to executing training and evaluation tasks.

Getting Started: Requirements

To begin your journey with this implementation, you need to ensure that your environment meets the required specifications:

• Python version: 3.6.x
• PyTorch version: 1.6.0 (versions from 1.1.0 to 1.6.0 are compatible, but mixed precision training will be disabled)
• CUDA version: 10.1

After confirming your environment setup, you can install additional dependencies by executing the following command:

pip install -r requirements.txt

Setting Up RANSAC Five-Point Algorithm

If you want to use the RANSAC five-point algorithm, navigate to the appropriate directory and install the necessary package:

cd RANSAC_FivePoint
python setup.py install --user

This will install a CUDA extension termed essential_matrix, which is vital for the algorithm to function correctly. This setup has been tested primarily under Ubuntu with CUDA 10.1.

Downloading Models and Datasets

To reproduce the results mentioned in the paper, you will need access to the KITTI dataset:

• Download KITTI RAW data
• Download KITTI official depth maps (14GB)

Upon downloading, unzip these files into a folder and update the cfg.GT_DEPTH_DIR in kitti.yml to reflect your new directory. Additionally, grab the split files and extract them into the root of the KITTI raw data.

Executing Training and Evaluation

With everything in place, you can proceed to train your model with this command:

python main.py -b 32 --lr 0.0005 --nlabel 128 --fix_flownet --data PATHTOYOURKITTIDATASET --cfg cfgskitti.yml --pretrained-depth depth_init.pth.tar --pretrained-flow flow_init.pth.tar

To evaluate your model, use the following command:

python main.py -v -b 1 -p 1 --nlabel 128 --data PATHTOYOURKITTIDATASET --cfg cfgskitti.yml --pretrained kitti.pth.tar

The expected metrics for a successful evaluation are an abs_rel around 0.053 and an RMSE close to 2.22 when using official ground truth depth. Adjust your configuration file as necessary based on the evaluation split preference.

Troubleshooting Common Issues

As with any implementation, you may run into obstacles. Here are some common challenges along with suggestions:

• Installation Errors: Ensure that all dependencies are correctly installed and that you are using the specified Python and CUDA versions.
• KITTI Data Handling: Verify that the KITTI dataset and depth maps are properly unzipped and the paths in your configuration files are correctly set.
• Evaluation Not as Expected: If your evaluation results do not meet the expected benchmarks, check the data preprocessing steps and ensure that all necessary flags in your configuration files are correctly set.

If you encounter persistent issues, feel free to seek further assistance from the community or look for updates on the repository. For more insights, updates, or to collaborate on AI development projects, stay connected with fxis.ai.

Conclusion

With the right setup and guidance, you can effectively leverage the techniques outlined in Deep Two-View Structure-from-Motion Revisited. Remember to keep experimenting and exploring, as the world of computer vision holds endless possibilities for innovation and discovery.

At fxis.ai, we believe that such advancements are crucial for the future of AI, as they enable more comprehensive and effective solutions. Our team is continually exploring new methodologies to push the envelope in artificial intelligence, ensuring that our clients benefit from the latest technological innovations.