Understanding and predicting navigable spaces from a single RGB image has been a daunting challenge in computer vision. The breakthrough method known as Footprints, introduced by Jamie Watson and his colleagues, promises greater efficiency in visual rendering and agent navigation. This blog will guide you through the fundamentals of implementing this method, its setup, and training requirements.

Understanding the Footprints Method

Imagine that you are a photographer standing on a mountain with a spectacular view. You can see clearly what’s in front of you, but what lies beyond the horizon is hidden from view. If you want to guide someone through the valley below based only on what you see, you would need to predict the terrain that isn’t visible. The Footprints method accomplishes this for robots and virtual characters by estimating both visible and hidden ground topographies from just one color image. By employing advanced machine learning algorithms, it enables virtual beings to explore their environments more realistically.

Setting Up Your Environment

To implement Footprints, you will need a conducive environment:

• Ensure you have PyTorch 1.3.1 installed.
• Utilize the provided environment.yml and requirements.txt files.
• Create and activate a new conda environment using the command:

conda env create -f environment.yml -n footprints
conda activate footprints

Making Predictions

The library provides three pretrained models for different datasets:

• KITTI: Trained on the KITTI dataset (Resolution: 192×640)
• Matterport: Trained on the indoor Matterport dataset (Resolution: 512×640)
• Handheld: Trained using handheld stereo footage (Resolution: 256×448)

You can predict traversable space using the following commands:

# Single image prediction
python -m footprints.predict_simple --image test_data/cyclist.jpg --model kitti

# Multi image prediction
python -m footprints.predict_simple --image test_data --model handheld

By default, the predictions will be saved in the predictions folder.

Training Your Model

If you want to train a model tailored to your dataset, follow the steps outlined below:

• Download the KITTI and Matterport datasets.
• Edit the paths.yaml file to point to your raw data directories.
• Train the models using the specified commands:

# Train a KITTI model
CUDA_VISIBLE_DEVICES=X python -m footprints.main --training_dataset kitti --log_path your_log_path --model_name your_model_name

# Train a Matterport model
CUDA_VISIBLE_DEVICES=X python -m footprints.main --training_dataset matterport --height 512 --width 640 --log_path your_log_path --batch_size 8 --model_name your_model_name

Troubleshooting

If you encounter issues while setting up or running the Footprints system, here are a few common troubleshooting tips:

• Ensure you have the correct version of PyTorch and your libraries installed.
• Double-check your paths.yaml file for accurate paths.
• For issues related to GPU processing, ensure that proper CUDA drivers are installed.
• If predictions are not saving, revisit the command flags or check permissions for the folders.
• For more insights, updates, or to collaborate on AI development projects, stay connected with fxis.ai.

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.