Welcome to this guide on implementing the Planar Reconstruction model based on the CVPR 2019 paper titled Single-Image Piece-wise Planar 3D Reconstruction via Associative Embedding. This article will walk you through the steps to install the necessary dependencies, prepare your data, and run the training and evaluation of the model. Let’s embark on this exciting journey into 3D reconstruction!

Getting Started

First, we need to install the required software components before diving into the reconstruction model.

Installation

• Clone the repository and use git-lfs to fetch the trained model, or you can download it here:

git clone git@github.com:svip-lab/PlanarReconstruction.git

• We use Python 3; thus, create an Anaconda environment and install the dependencies:

conda create -y -n plane python=3.6
conda activate plane
conda install -c menpo opencv
pip install -r requirements.txt

Downloading and Converting Data

After installing, the next step is to obtain the data needed for training and testing.

• Download the *.tfrecords* files for both training and testing, converted by PlaneNet. Then convert the *.tfrecords* to *.npz* files:

python data_tools/convert_tfrecords.py --data_type=train --input_tfrecords_file=path/to/planes_scannet_train.tfrecords --output_dir=path/to/save/processed/data
python data_tools/convert_tfrecords.py --data_type=val --input_tfrecords_file=path/to/planes_scannet_val.tfrecords --output_dir=path/to/save/processed/data

Training the Model

Now that our data is ready, let’s train the network. Execute the command below:

python main.py train with dataset.root_dir=path/to/save/processed/data

Evaluating the Model

Once the training completes, you’ll want to evaluate the model’s performance using the following command:

python main.py eval with dataset.root_dir=path/to/save/processed/data resume_dir=path/to/pretrained.pt dataset.batch_size=1

Making Predictions

Finally, to make predictions on a single image, execute:

python predict.py with resume_dir=pretrained.pt image_path=path/to/image

Troubleshooting

If you encounter issues during installation or execution, consider the following troubleshooting tips:

• Ensure that you have installed git-lfs correctly to avoid issues with fetching models.
• Make sure your Anaconda environment is activated before running the commands to prevent missing dependency errors.
• If the model takes too long to train or crashes, consider checking your hardware specifications, as insufficient resources may lead to failures.
• For more insights, updates, or to collaborate on AI development projects, stay connected with fxis.ai.

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.

Understanding the Code: An Analogy

Think of training a model like preparing a dish in a kitchen. Each step in the instructions, from gathering ingredients (downloading data) to the actual cooking process (training and evaluating the model), comes together to create a delicious, finished meal (the 3D reconstruction output).

• Gathering ingredients: You first need to collect all necessary data files, much like a chef would gather ingredients before cooking.
• Preparing the workspace: Setting up your environment (Anaconda setup) prepares your kitchen for cooking.
• Cooking: Training the model is akin to cooking, where the output (the delicious dish) depends on how well each step is executed.
• Tasting: Evaluating the model is like tasting the dish to ensure it’s been prepared correctly.
• Serving: Making predictions is like plating the dish, ready to be served and enjoyed by the guests.

Acknowledgements

We appreciate Chen Liu for his significant contributions and repositories that enhanced our work.

Conclusion

With the above steps, you should be well on your way to successfully implementing the Planar Reconstruction model. Enjoy turning your images into detailed 3D reconstructions!