If you’re embarking on a journey into the realm of 3D modeling, especially with sparse-view settings, you’ve stumbled upon the gem that is LEAP (Liberate Sparse-view 3D Modeling from Camera Poses). This guide will take you through the installation process step-by-step, ensuring a smooth experience.
Installation Steps
To begin your venture with LEAP, follow these concise steps:
- Create a new conda environment:
conda create --name leap python=3.9 - Activate the environment:
conda activate leap - Install PyTorch: You can either install the version specified here or use your own. The LEAP project uses PyTorch version 2.0.1.
conda install pytorch torchvision torchaudio pytorch-cuda=11.7 -c pytorch -c nvidia - Install PyTorch3D: Follow the instructions on the official GitHub page for installation. The LEAP project uses version 0.7.4.
- (Optional) Install Flash Attention: Installing this can help with training on limited GPU memory, but it might lead to slightly worse performance. Follow the guide on GitHub for the 1.0.7 version.
- Install required packages:
pip install -r requirements.txt
Utilizing Pre-trained Weights
LEAP facilitates your modeling journey with pre-trained weights. Download the weights from the following datasets:
Running the Demo
To run the LEAP demo:
- Download the pre-trained weights and modify the pre-trained weight path at line 34 of demo.py.
- Execute the demo script using .demo.sh.
- Feel free to capture your images and use the segmented images as inputs.
Training LEAP
Ready to train?
- Download the dataset by visiting the appropriate links for Omniobject3D, FORGE, and Zero123.
- Adjust the self.root in the data loaders accordingly.
- Use .train.sh to start training and ensure to modify your training configuration as needed. Be aware of space requirements which can go up to 300GB.
Evaluation Process
For evaluating your model, simply use .eval.sh and adjust your evaluation configuration accordingly.
Troubleshooting Guide
While working with LEAP, you might encounter some common issues. Here’s how to troubleshoot effectively:
- Model Inaccuracies: If the model trained on the Omniobject3D struggles with real images, utilize the Kubric pre-trained weights for a more reliable outcome.
- Overfitting Issues: Remember that the model may overfit the training intrinsics. Use the training intrinsics while evaluating novel datasets.
- Weighting Methodologies: There’s no release for the objaverse pre-trained model since it requires extensive resources and has noisy samples.
- If you’re constantly running into problems, for up-to-date solutions, or to collaborate on AI development projects, stay connected with fxis.ai.
Conclusion
At fxis.ai, we believe that advancements like LEAP are crucial for the future of AI, facilitating 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.