Splatter Image: Ultra-Fast Single-View 3D Reconstruction
The Splatter Image project is an innovative and rapid approach to single-view 3D reconstruction, proposed as part of the CVPR 2024 conference. This project aims to transform how 3D models are constructed from single-viewpoint images using cutting-edge technology and novel methodologies.
Key Features and Updates
Since its initial release, the Splatter Image project has undergone numerous enhancements:
- Universal Object Reconstruction: The system is now capable of reconstructing virtually any object. This feat was accomplished by training an open-category model using Objaverse in just 7 GPU days.
- Interactive Demo: A hands-on demo is available where users can upload images of any object, and the model will generate its 3D reconstruction. Running the demo locally is recommended for faster performance.
- Comprehensive Dataset Models: Models across six different datasets are available, covering Objaverse, multi-category ShapeNet, CO3D hydrants, CO3D teddybears, ShapeNet cars, and ShapeNet chairs.
- State-of-the-Art Performance: The system has achieved state-of-the-art results on the multi-category ShapeNet dataset.
- Multi-GPU Training Support: Allows for accelerated model training by utilizing multiple GPUs.
- No Preprocessing Required for CO3D: Camera pose preprocessing is no longer necessary for datasets related to CO3D.
Installation Steps
To use the Splatter Image project locally, follow these steps:
- Create an Environment: Use Conda to create a dedicated environment.
conda create --name splatter-image conda activate splatter-image - Install PyTorch: Follow the official instructions to install PyTorch, ensuring compatibility with the recommended versions for the project.
- Install Additional Requirements:
pip install -r requirements.txt - Gaussian Splatting Renderer: Install the necessary library for rendering Gaussian point clouds.
- Install Pytorch3D (for CO3D data): This step is necessary if you plan to work with CO3D data.
Data Management
The project supports various datasets, including ShapeNet cars and chairs, CO3D hydrants and teddybears, multi-category ShapeNet, Objaverse, and Google Scanned Objects. Each requires specific steps for data preparation and configuration that ensure the system can process and evaluate these datasets effectively.
Model Use and Evaluation
The project offers pretrained models for immediate use, available through Huggingface Models. Users can evaluate datasets using these pre-trained models or train and test their custom models. Evaluation scripts are provided to facilitate both qualitative and quantitative analyses of model performance.
Training Procedure
Training the models involves a two-stage process. Initially, the model is trained without LPIPS, focusing on performance and accuracy. This phase is followed by a fine-tuning stage, incorporating LPIPS to refine the model further. There are also options to train for specific views, directing the model to focus on particular dataset configurations or parameters.
Code Structure and Usage
The project's structure is designed for ease of use, with separate scripts for training and evaluation, as well as clearly defined dataset and model files. Camera conventions follow standard practices ensuring compatibility with common tools like COLMAP and OpenCV.
Academic Contributions
The Splatter Image project is supported by multiple research scholarships and contributions from various academic institutions, highlighting its significance in advancing the field of 3D reconstruction.
Conclusion
The Splatter Image project not only showcases significant advancements in 3D reconstruction technology but also provides a practical, user-friendly platform for researchers and developers to explore and utilize these innovations in various applications.