Welcome to this user-friendly guide on the Inverse Compositional Algorithm, as presented in our CVPR 2019 paper. The project highlights contributions from Zhaoyang Lv, Frank Dellaert, James M. Rehg, and Andreas Geiger. Together, we explore this fascinating algorithm and how to set it up for your own research.

Getting Started: Project Setup

The first step to diving into the Inverse Compositional Algorithm is to properly set up your programming environment. This code has been developed using the following technologies:

• Pytorch version 1.0
• CUDA version 9.0
• OS: Ubuntu 16.04

To ensure compatibility, we suggest creating a dedicated Anaconda environment following these steps:

bash
conda env create -f setupenvironment.yml
conda activate deepICN # Activates the environment

Running a Quick Inference Example

With your environment set up, you can perform a quick inference to test the algorithm using a pre-trained model. Execute the following command:

python run_example.py

However, be mindful of the depth reader! Switching datasets may require scaling adjustments. The pre-trained model is optimized for egocentric motion estimation using the TUM dataset, which could introduce variations in results when applied to object-centric datasets.

Preparing Your Datasets

Your next step involves preparing the datasets essential for the Inverse Compositional Algorithm:

• For the TUM RGBD Dataset, download it from TUM RGBD and set up a symbolic link:

ln -s $YOUR_TUM_RGBD_DIR codedatadata_tum

• Download the MovingObjects3D Dataset from MovingObjs3D and create the corresponding symbolic link:

ln -s $YOUR_MOV_OBJS_3D_DIR codedatadata_objs3D

Training the Algorithm

To train your model using the TUM RGBD dataset, run:

python train.py --dataset TUM_RGBD

You can also configure specific settings by adding parameters like so:

python train.py --dataset TUM_RGBD --encoder_name ConvRGBD2 --mestimator MultiScale2w --solver Direct-ResVol --keyframes 1,2,4,8

Monitor your training progress via TensorBoard:

tensorboard --log logsTUM_RGBD --port 8000

Troubleshooting Tips

If you encounter issues during setup or execution, here are a few troubleshooting tips:

• Ensure that the correct version of Pytorch and CUDA are installed.
• Verify that your symbolic links are correctly pointing to the dataset directories.
• If you get a compatibility error with matrix inverse functions, consider updating to a compatible library or environment.

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Evaluating Your Model

To see how your pretrained model performs, use the following command:

python evaluate.py --dataset TUM_RGBD --trajectory fr1rgbd_dataset_freiburg1_360 --encoder_name ConvRGBD2 --mestimator MultiScale2w --solver Direct-ResVol --keyframes 1 --checkpoint trained_modelsTUM_RGBD_ABC_final.pth.tar

Conclusion

With the Inverse Compositional Algorithm, you can explore advanced techniques in motion estimation. Remember, the evaluation and training configurations share the same setup process, allowing you to easily examine different trajectories and configurations.

Final Thoughts

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.