VGGSfM: Visual Geometry Grounded Deep Structure From Motion
Introduction
VGGSfM is an innovative project that focuses on the creation of 3D reconstructions from images using a method known as Structure From Motion (SfM). The project is a collaborative effort involving notable institutions such as Meta AI Research GenAI and the University of Oxford. The principal contributors to this project include Jianyuan Wang, Nikita Karaev, Christian Rupprecht, and David Novotny.
This fascinating technology has achieved recognition by ranking first in the CVPR24 IMC Challenge for camera pose estimation. The project continually evolves, with recent updates including the ability to export dense point clouds and added functionalities for handling dynamic objects in video sequences.
Key Features and Updates
VGGSfM has undergone several updates to enhance its capabilities:
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Dense Point Clouds: As of September 2024, users can export a dense point cloud, which provides a more detailed 3D representation of a scene.
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Flexible Training: Instructions have been added for training a Gaussian splatting model with the results provided by VGGSfM.
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Video Reconstruction: A video runner feature supports the reconstruction of over 1000 frames from videos, effectively recovering camera poses and point clouds from dynamic sequences using masks to filter out moving objects.
Installation
Setting up VGGSfM is straightforward with a simple installation script. It requires setting up a conda environment with Python 3.10, PyTorch 2.1, and CUDA 12.1. The script also installs essential libraries like pytorch3d, lightglue, pycolmap, poselib, and visdom for visualization.
Using VGGSfM
VGGSfM provides an accessible way to perform 3D reconstructions:
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Pre-trained Model: Users can download the pre-trained model from Hugging Face if preferred.
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Running a Demo: The project includes demo scripts that allow users to start with various examples, adjusting settings to suit their data and visualization needs.
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Visualization Tools: For ease of visualization, options like Gradio and Visdom are provided, allowing users to view their results conveniently in web browsers or through Visdom’s interface.
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Personal Data Utilization: Users can work with their own data by organizing images properly and running the demo with customized settings.
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Generating Denser Point Clouds: This feature enhances the accuracy of models by adding more points in the cloud, improving the 3D reconstruction detail without extensive computation.
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Handling Dynamic Objects: The system can manage frames containing moving objects. By using masks to filter dynamic pixels, the accuracy of the static portions of the scene is maintained.
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Sequencing Video Input: For video input, VGGSfM can process frames sequentially in a sliding window manner, efficiently handling large numbers of frames.
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Training a Gaussian Splatting Model: The results from VGGSfM can be used to train models using the gsplat framework, providing users with customized 3D scene representations based on their unique data sets.
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Dense Depth Prediction (Beta): This feature aligns dense depth maps using outputs from VGGSfM, enhancing the depth perception of reconstructed scenes.
Frequently Asked Questions
VGGSfM provides a comprehensive FAQ section addressing common issues such as handling memory errors, managing sparse data, and optimizing camera settings. These resources are vital for ensuring a smooth user experience.
Conclusion
VGGSfM stands out as a powerful tool for researchers and developers working with 3D reconstruction and computer vision. Its flexibility and user-friendly features make it accessible for both academic and practical applications. By continually advancing and offering new capabilities, VGGSfM opens new doors for innovation in visual geometry processing.