

Vggsfm
Overview :
VGGSfM is a depth learning-driven 3D reconstruction technology aimed at reconstructing the camera pose and 3D structure of a scene from a set of unconstrained 2D images. This technology uses a fully differentiable deep learning framework for end-to-end training. It extracts reliable pixel-level trajectories using depth 2D point tracking technology, while restoring all cameras based on image and trajectory features, and optimizing camera and triangulated 3D points through a differentiable bundle adjustment layer. VGGSfM achieves state-of-the-art performance in three popular datasets: CO3D, IMC Phototourism, and ETH3D.
Target Users :
VGGSfM is primarily designed for computer vision researchers and developers, especially those who focus on 3D reconstruction and deep learning technologies. This technology can be used in areas such as augmented reality, virtual reality, and autonomous driving, helping them extract more precise 3D structural information from 2D images.
Use Cases
3D reconstruction on the CO3D dataset
Camera and point cloud reconstruction on the IMC Phototourism dataset
Camera pose and 3D structure reconstruction on the ETH3D dataset
Features
Extract 2D trajectories from input images
Reconstruct cameras using image and trajectory features
Initialize point cloud based on these trajectories and camera parameters
Apply bundle adjustment layer for reconstruction refinement
Fully differentiable framework design
Apply photo reconstruction in field applications, demonstrating estimated point cloud and cameras
Qualitative visualization of camera and point cloud reconstruction on Co3D and IMC Phototourism
In each row, the query image and query point are on the far left, and predicted trajectory points are shown on the right
How to Use
1. Prepare a set of unconstrained 2D images as input
2. Use the VGGSfM model to extract 2D trajectories from the input images
3. Reconstruct cameras using the extracted trajectories and image features
4. Initialize point cloud based on trajectories and camera parameters
5. Apply bundle adjustment layer for point cloud and camera reconstruction refinement
6. Evaluate and optimize reconstruction results for accuracy and reliability
7. Apply the reconstructed 3D structure to related fields, such as augmented reality, virtual reality, etc.
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