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Updated: Jun 18, 2026
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Published on: August 16, 2021
DynSUP: Dynamic Gaussian Splatting From an Unposed Image Pair
Summary
This study introduces a novel method for dynamic 3D scene reconstruction using only two images without prior camera poses. It enables high-fidelity novel view synthesis and accurate motion modeling in dynamic environments.
Area of Science:
- Computer Vision
- Computer Graphics
- 3D Reconstruction
Background:
- 3D Gaussian Splatting excels in static scenes with multiple images and known camera poses.
- Dynamic scenes, sparse views, and unknown poses present significant challenges due to limited geometric constraints.
Purpose of the Study:
- To develop a method for dynamic 3D scene reconstruction from only two unposed images.
- To enable high-fidelity novel view synthesis and accurate motion modeling in dynamic environments.
Main Methods:
- Object-level two-view bundle adjustment to decompose dynamic scenes and estimate camera/object motion.
- SE(3) field-driven Gaussian training for fine-grained, per-Gaussian motion modeling.
Main Results:
- Achieved high-fidelity novel view synthesis for dynamic scenes.
- Accurately preserved temporal consistency and object motion.
- Outperformed state-of-the-art methods on synthetic and real-world datasets.
Conclusions:
- The proposed method effectively reconstructs dynamic 3D scenes from minimal, unposed input.
- It overcomes limitations of existing methods in handling dynamic environments and unknown camera poses.
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