DynSUP: Dynamic Gaussian Splatting from An Unposed Image Pair
DynSUP uses two unposed images to achieve dynamic Gaussian splatting, significantly enhancing dynamic scene synthesis.
Key Findings
Methodology
The paper introduces DynSUP, a novel dynamic Gaussian splatting method that fits Gaussians in dynamic environments using only two unposed images. The method includes object-level two-view bundle adjustment and SE(3) field-driven Gaussian training. Object-level bundle adjustment decomposes dynamic scenes into piece-wise rigid components and jointly estimates camera poses and dynamic object motions. SE(3) field-driven Gaussian training enables fine-grained motion modeling through learnable per-Gaussian transformations.
Key Results
- On the KITTI dataset, DynSUP achieved a PSNR of 24.71 and SSIM of 0.82, significantly outperforming 4DGS and SC-GS.
- On the Kubric dataset, DynSUP achieved a PSNR of 33.86, SSIM of 0.97, and LPIPS of 0.03, demonstrating superior performance.
- Ablation studies confirmed the critical role of object-level bundle adjustment and SE(3) field in performance enhancement.
Significance
This research breaks the dependency on multiple views and known poses in dynamic scene reconstruction, providing a new method for high-quality dynamic scene synthesis with only two unposed images. It has significant applications in virtual reality and autonomous driving, addressing the challenge of insufficient geometric constraints in dynamic scenes.
Technical Contribution
The technical contributions include the introduction of object-level two-view bundle adjustment and SE(3) field-driven Gaussian training, enabling high-fidelity novel view synthesis in dynamic environments. Unlike existing methods, DynSUP achieves high-quality reconstruction and rendering without known poses in dynamic scenes.
Novelty
DynSUP is the first method to fit Gaussians in dynamic environments using only two unposed images. Compared to existing methods, DynSUP achieves finer motion modeling through object-level bundle adjustment and SE(3) field-driven Gaussian training.
Limitations
- In fast-moving scenes, optical flow estimation may be inaccurate, affecting pose estimation.
- Complex dynamic scenes may require higher computational resources.
Future Work
Future research directions include improving robustness in fast-moving scenes, extending to more views, and validating in more complex dynamic environments.
AI Executive Summary
DynSUP achieves dynamic Gaussian splatting using two unposed images, significantly enhancing dynamic scene synthesis. Existing 3D Gaussian splatting methods typically assume static scenes or known camera poses, limiting their application in dynamic environments. DynSUP addresses this issue through object-level two-view bundle adjustment and SE(3) field-driven Gaussian training.
Object-level two-view bundle adjustment decomposes dynamic scenes into piece-wise rigid components, jointly estimating camera poses and dynamic object motions. SE(3) field-driven Gaussian training enables fine-grained motion modeling through learnable per-Gaussian transformations. This method significantly outperforms existing methods on the KITTI and Kubric datasets, demonstrating superior performance.
DynSUP has significant applications in virtual reality and autonomous driving, addressing the challenge of insufficient geometric constraints in dynamic scenes. However, optical flow estimation may be inaccurate in fast-moving scenes, and future research will focus on improving robustness and extending application scenarios.
Deep Analysis
Background
3D reconstruction technology has widespread applications in computer vision. Recently, techniques like Neural Radiance Fields (NeRF) and 3D Gaussian Splatting (3D-GS) have made significant progress in novel view synthesis. However, these methods typically rely on multiple views and known camera poses, limiting their application in dynamic scenes. Dynamic scene reconstruction faces challenges due to high-dimensional parameter space and complex geometric configurations.
Core Problem
The core problem in dynamic scene reconstruction is achieving high-quality view synthesis with insufficient geometric constraints. Traditional methods rely on multiple views and known poses, which are often difficult to satisfy in practical applications. Solving this problem is crucial for advancing fields like virtual reality and autonomous driving.
Innovation
The core innovations of DynSUP include: 1) Object-level two-view bundle adjustment, which decomposes dynamic scenes into piece-wise rigid components for joint estimation of camera poses and dynamic object motions; 2) SE(3) field-driven Gaussian training, enabling fine-grained motion modeling through learnable per-Gaussian transformations. These innovations allow DynSUP to achieve high-fidelity novel view synthesis in dynamic environments.
Methodology
- �� Object-level two-view bundle adjustment: decomposes scenes into piece-wise rigid components, jointly optimizing reprojection loss and depth regularization loss.
- �� SE(3) field-driven Gaussian training: each Gaussian maintains an individual SE(3) transformation, enabling fine-grained motion modeling.
- �� Optimize camera poses and per-object SE(3) ratios for test image alignment.
Experiments
Experiments were conducted on the KITTI and Kubric datasets, using metrics like PSNR, SSIM, and LPIPS for evaluation. Compared to methods like 4DGS and SC-GS, DynSUP excels in dynamic scenes, especially with only two unposed images, significantly enhancing view synthesis quality.
Results
On the KITTI dataset, DynSUP achieved a PSNR of 24.71 and SSIM of 0.82, significantly outperforming 4DGS and SC-GS. On the Kubric dataset, DynSUP achieved a PSNR of 33.86, SSIM of 0.97, and LPIPS of 0.03, demonstrating superior performance. Ablation studies confirmed the critical role of object-level bundle adjustment and SE(3) field in performance enhancement.
Applications
DynSUP has significant applications in virtual reality and autonomous driving. By using only two unposed images, this method can achieve high-quality view synthesis in dynamic environments, providing new possibilities for these fields.
Limitations & Outlook
In fast-moving scenes, optical flow estimation may be inaccurate, affecting pose estimation. Complex dynamic scenes may require higher computational resources. Future research will focus on improving robustness and extending application scenarios.
Plain Language Accessible to non-experts
Imagine you're filming a dynamic stage performance. Traditional methods require many cameras from different angles and knowledge of each camera's position. DynSUP is like a smart director who can reconstruct the entire dynamic scene with just two camera shots. It analyzes each performer's movements and predicts their positions and poses at different times. This way, even with only two shots, it can generate a high-quality dynamic scene.
ELI14 Explained like you're 14
Imagine you're playing a game with lots of moving characters. Usually, you need many cameras to capture each character's movements, but DynSUP is like a super-smart game engine that can figure out what all the characters are doing with just two camera shots. It analyzes each character's movements and guesses what they'll do next. This way, you can see a complete, dynamic game world without needing lots of cameras!
Glossary
Gaussian Splatting
A technique for 3D scene reconstruction using Gaussian functions to represent points in the scene.
Used in this paper for dynamic scene reconstruction.
Bundle Adjustment
An optimization technique for simultaneously adjusting camera parameters and 3D point positions.
Used for object-level two-view bundle adjustment.
SE(3) Transformation
A mathematical model representing rigid body motion, including rotation and translation.
Used to drive motion modeling in Gaussian training.
Optical Flow
A technique for estimating pixel motion in image sequences.
Used to estimate motion in dynamic scenes.
PSNR
Peak Signal-to-Noise Ratio, a measure of image quality.
Used to evaluate the performance of the DynSUP method.
Open Questions Unanswered questions from this research
- 1 How to improve robustness in more complex dynamic scenes?
- 2 How to enhance optical flow estimation accuracy in fast-moving scenes?
Applications
Immediate Applications
Virtual Reality
DynSUP can be used for dynamic scene synthesis in virtual reality, enhancing user experience.
Long-term Vision
Autonomous Driving
In autonomous driving, DynSUP can be used for real-time dynamic scene reconstruction, improving vehicle perception of the environment.
Abstract
Recent advances in 3D Gaussian Splatting have shown promising results. Existing methods typically assume static scenes and/or multiple images with prior poses. Dynamics, sparse views, and unknown poses significantly increase the problem complexity due to insufficient geometric constraints. To overcome this challenge, we propose a method that can use only two images without prior poses to fit Gaussians in dynamic environments. To achieve this, we introduce two technical contributions. First, we propose an object-level two-view bundle adjustment. This strategy decomposes dynamic scenes into piece-wise rigid components, and jointly estimates the camera pose and motions of dynamic objects. Second, we design an SE(3) field-driven Gaussian training method. It enables fine-grained motion modeling through learnable per-Gaussian transformations. Our method leads to high-fidelity novel view synthesis of dynamic scenes while accurately preserving temporal consistency and object motion. Experiments on both synthetic and real-world datasets demonstrate that our method significantly outperforms state-of-the-art approaches designed for the cases of static environments, multiple images, and/or known poses. Our project page is available at https://colin-de.github.io/DynSUP/.