HybridGS: Decoupling Transients and Statics with 2D and 3D Gaussian Splatting (2412.03844v4)

Abstract: Generating high-quality novel view renderings of 3D Gaussian Splatting (3DGS) in scenes featuring transient objects is challenging. We propose a novel hybrid representation, termed as HybridGS, using 2D Gaussians for transient objects per image and maintaining traditional 3D Gaussians for the whole static scenes. Note that, the 3DGS itself is better suited for modeling static scenes that assume multi-view consistency, but the transient objects appear occasionally and do not adhere to the assumption, thus we model them as planar objects from a single view, represented with 2D Gaussians. Our novel representation decomposes the scene from the perspective of fundamental viewpoint consistency, making it more reasonable. Additionally, we present a novel multi-view regulated supervision method for 3DGS that leverages information from co-visible regions, further enhancing the distinctions between the transients and statics. Then, we propose a straightforward yet effective multi-stage training strategy to ensure robust training and high-quality view synthesis across various settings. Experiments on benchmark datasets show our state-of-the-art performance of novel view synthesis in both indoor and outdoor scenes, even in the presence of distracting elements.

Evaluating HybridGS: A Novel Approach to Novel View Synthesis

The paper "HybridGS: Decoupling Transients and Statics with 2D and 3D Gaussian Splatting" presents a novel methodology for synthesizing novel views of 3D scenes, particularly focusing on separating transient and static components using a hybrid representation. The proposed approach, HybridGS, leverages both 3D Gaussian Splatting (3DGS) and 2D Gaussians to enhance the modeling and rendering of scenes with transient objects, commonly found in casually captured images.

Core Contributions

The core contributions of this paper are as follows:

1. Hybrid Representation: The introduction of a hybrid model that incorporates 2D Gaussians for transient objects specific to each image, while employing traditional 3D Gaussians for consistent representation of the static components of the scene. This dual approach ensures that transient objects, which often violate multi-view consistency, are represented as view-specific planar elements.
2. Multi-view Regulated Supervision: A novel training supervision mechanism for 3DGS that uses overlapping regions across multiple views to better distinguish between transient and static elements, significantly improving view synthesis accuracy.
3. Multi-stage Training Strategy: The authors propose a structured training approach that consists of three stages: warm-up, iterative training, and joint fine-tuning. This strategy is designed to integrate both 2D and 3D Gaussians efficiently, ensuring high-quality scene reconstruction and stable convergence during the training process.

Experimental Evaluation and Results

Extensive experimental evaluations demonstrate that HybridGS achieves state-of-the-art performance on benchmark datasets, including NeRF On-the-go and RobustNeRF. Quantitatively, HybridGS shows superior performance in PSNR, SSIM, and LPIPS metrics compared to existing methods. The hybrid model provides substantial improvements in scenes with varying levels of occlusions.

The qualitative assessment further highlights the clarity and detail in the rendered images, especially in complex scenes with significant dynamic elements. The results indicate that HybridGS is able to effectively reduce artifacts and sharpen boundaries in novel view synthesis, outperforming previous methods.

Implications and Future Directions

The implications of this research are significant for applications involving virtual reality, augmented reality, and robotics, where accurate rendering of dynamic scenes is crucial. By effectively decoupling transient and static elements, HybridGS could support more realistic and detailed reconstructions in real-time applications.

The paper also discusses current limitations, particularly in handling variations in illumination within unconstrained photo collections. Future research avenues could involve integrating appearance embedding modules to better model photometric changes.

Conclusion

"HybridGS: Decoupling Transients and Statics with 2D and 3D Gaussian Splatting" offers a rigorous and effective solution for the challenges posed by transients in novel view synthesis. The hybrid approach not only enhances the quality of scene reconstructions but also provides a robust framework for integrating both transient and static elements within a single model. This work sets a new benchmark for future studies in the area of 3D scene representation and rendering.