LiteVGGT is a 3D vision foundation model that significantly boosts vanilla VGGT's performance by achieving up to 10x speedup and substantial memory reduction. This enables efficient processing of large-scale scenes (up to 1000 images) for 3D reconstruction, while maintaining high accuracy in camera pose and point cloud prediction. The method introduces a geometry-aware cached token merging strategy to optimize anchor token selection and reuse merge indices, preserving key geometric information with minimal accuracy impact.

This model was presented in the paper: LiteVGGT: Boosting Vanilla VGGT via Geometry-aware Cached Token Merging.

For 1000 input images, LiteVGGT achieves a 10× speedup over VGGT while maintaining high accuracy in camera pose and point cloud prediction. Its scalability and robustness make large-scale scene reconstruction more efficient and reliable.

To quickly try out LiteVGGT for 3D reconstruction, follow these steps:

First, create a virtual environment using Conda, clone this repository to your local machine, and install the required dependencies.

Install the Transformer Engine package following its official installation requirements (see https://github.com/NVIDIA/TransformerEngine):

Then, download our LiteVGGT checkpoint that has been finetuned and TE-remapped:

Finally: