In the rapidly evolving world of computer graphics, the need for efficient rendering methods cannot be overstated. One innovative solution is BakedSDF, an unofficial PyTorch implementation of Meshing Neural SDFs for real-time view synthesis. Here’s a user-friendly guide on how to set it up and run it seamlessly.

Installation

Getting started with BakedSDF involves a few straightforward installation steps:

• First, you need to install the necessary packages:

pip install torch torchvision

pip install git+https://github.com/NVlabs/tiny-cuda-nn#subdirectory=bindings

• Next, install the required dependencies using:

pip install -r requirements.txt

• If you need to work with COLMAP for data preparation, you can find alternative installation options on the COLMAP website.

Data Preparation

To prepare your data using COLMAP, follow these steps:

• Ensure COLMAP is installed. Refer to the installation instructions here.
• Create a folder named images and place your images inside.
• Run the following script to convert images to poses:

python scripts/imgs2poses.py .

• As an example, if your images are in .load/mbvs_dog, you would run:

python scripts/imgs2poses.py .load/mbvs_dog

• Existing data following the required file structure can be used, as long as the images are stored in the images folder and there’s a sparse folder for the COLMAP output.

Running the BakedSDF!

To start the BakedSDF process, execute the following commands:

• For training:

python launch.py --config configs/neus-colmap.yaml --gpu 0 --train dataset.root_dir=$1

• For resuming weights:

python launch.py --config configs/bakedsdf-colmap.yaml --gpu 0 --train dataset.root_dir=$1 --resume_weights_only --resume latest

Exporting BakedSDF

Once the training is done, export your BakedSDF with the following command:

python export.py --exp_dir .exp$exp_name$trail-name

For instance, to export the neus-colmap data, use:

python export.py --exp_dir .exp/neus-colmap-stump@20230907-133647

The export results will be saved in the .results directory in glb format.

Integrating BakedSDF into Your Application

To utilize BakedSDF in your application, follow these instructions:

On Unity and Unreal

You can utilize BakedSDF2FBX to convert the exported glb files and integrate them into your Unity and Unreal projects:

• UnityBakedSDF: Unity integration.
• UnrealBakedSDF: Unreal integration.

On the Web

A local web viewer will be available soon!

Troubleshooting

If you encounter issues during the installation or execution process, here are some troubleshooting tips:

• Ensure that all necessary libraries are installed correctly without errors. Re-run the installation steps if needed.
• If you’re facing GPU-related issues, confirm that the correct GPU is being utilized in your settings.
• For questions specific to COLMAP, refer to the community or documentation for guidance.
• If you need further assistance or insights, feel free to connect with us at 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 with an Analogy

Think of the BakedSDF process as baking a cake. Just like you need the right ingredients and steps to create a delicious cake, here we need the correct setup and clear instructions to generate our real-time view synthesis:

• Ingredients (Installation): These include the necessary packages like torch and torchvision, which serve as the flour and sugar for our cake.
• Preparation (Data Preparation): Placing images in the correct folder is like gathering your eggs and milk; everything needs to be in place before you begin cooking.
• Baking (Running BakedSDF): What happens when you put the cake in the oven? You follow the right steps and commands to train the model—this is where the magic happens.
• Decoration (Exporting): Finally, exporting the model is akin to frosting the cake, making it ready for presentation or use in your applications.

By following this guide, you can effectively navigate through the BakedSDF implementation and enhance your real-time rendering projects with ease!

For more insights, updates, or to collaborate on AI development projects, stay connected with fxis.ai.