RefGaussian: Disentangling Reflections from 3D Gaussian Splatting for Realistic Rendering (2406.05852v1)

Abstract: 3D Gaussian Splatting (3D-GS) has made a notable advancement in the field of neural rendering, 3D scene reconstruction, and novel view synthesis. Nevertheless, 3D-GS encounters the main challenge when it comes to accurately representing physical reflections, especially in the case of total reflection and semi-reflection that are commonly found in real-world scenes. This limitation causes reflections to be mistakenly treated as independent elements with physical presence, leading to imprecise reconstructions. Herein, to tackle this challenge, we propose RefGaussian to disentangle reflections from 3D-GS for realistically modeling reflections. Specifically, we propose to split a scene into transmitted and reflected components and represent these components using two Spherical Harmonics (SH). Given that this decomposition is not fully determined, we employ local regularization techniques to ensure local smoothness for both the transmitted and reflected components, thereby achieving more plausible decomposition outcomes than 3D-GS. Experimental results demonstrate that our approach achieves superior novel view synthesis and accurate depth estimation outcomes. Furthermore, it enables the utilization of scene editing applications, ensuring both high-quality results and physical coherence.

• The paper introduces RefGaussian, a method that disentangles transmitted and reflected components to significantly improve realistic 3D scene rendering.
• It employs an extended Gaussian representation with spherical harmonics and bilateral smoothness constraints to enhance both reflection quality and computational efficiency.
• Experimental results show improved PSNR, SSIM, and an average rendering speed of 81.856 FPS, outperforming traditional NeRF-based approaches.

RefGaussian: Disentangling Reflections from 3D Gaussian Splatting for Realistic Rendering

Introduction

The paper "RefGaussian: Disentangling Reflections from 3D Gaussian Splatting for Realistic Rendering" introduces an innovative methodology aimed at mitigating the challenges encountered in representing reflections within the 3D Gaussian Splatting (3D-GS) framework. Existing methods, including the pioneering Neural Radiance Fields (NeRF) and its variants, have demonstrated substantial advancements in novel view synthesis. However, these methods often struggle to accurately depict scenes containing reflective surfaces due to their computational complexity and representational limitations. In contrast, 3D-GS offers a promising alternative with enhanced efficiency for real-time applications, yet it too falls short in realistic reflection modeling. The proposed RefGaussian method addresses these shortcomings by introducing a seamless decomposition of transmitted and reflected components through an extended Gaussian representation.

Methodology

RefGaussian differentiates itself from traditional NeRF and 3D-GS techniques by effectively disentangling reflection modeling from the scene representation. The core of RefGaussian's methodology involves decomposing the scene into transmitted and reflected components, represented using spherical harmonics (SH). This decomposition, regulated by local smoothness constraints, facilitates a more accurate depiction of both components. Key innovations in the RefGaussian framework include:

• Extended Gaussian Representation: The method introduces additional reflection-related parameters to the Gaussian splatting. These parameters—reflection SH, reflection opacity, and reflection confidence—enable a joint training and synchronous rendering pipeline, incorporating both transmitted and reflected components.
• Bilateral Smoothness Priors: To address the inherent challenges in separating reflections without explicit mask guidance, RefGaussian employs a bilateral smoothness constraint, leveraging depth and color information to ensure cohesive scene decomposition.
• Efficient Rendering Pipeline: By extending the traditional 3D-GS framework, RefGaussian maintains the computational efficiency essential for real-time applications while achieving superior reflection modeling outcomes.

Experimental Results

The efficacy of RefGaussian is demonstrated through extensive experiments on the RFFR dataset, which comprises forward-facing scenes with pronounced reflections, along with additional tests on general scenes from Mip-NeRF360 and Tanks and Temples datasets. The results are compared against leading NeRF-based methods (NeRF, NeRFReN, NeRF-D) and 3D-GS.

Quantitatively, RefGaussian consistently outperforms these methods regarding PSNR and SSIM, particularly excelling in scenes with significant reflections such as mirrors and screens. Additionally, RefGaussian shows competitive performance on more general scenarios, underscoring its versatility.

Qualitatively, visual comparisons reveal RefGaussian's superior ability to reconstruct detailed and realistic scenes. The disentangled approach effectively separates transmitted and reflected components, avoiding the mutual perturbations observed in traditional methods. This is particularly evident in scenes containing intricate reflective details.

Moreover, RefGaussian achieves a substantial improvement in rendering speed, with an average of 81.856 FPS, making it significantly more efficient than NeRF-based models while maintaining comparable rendering quality.

Implications and Future Work

The practical implications of RefGaussian are manifold. It provides an effective solution for realistic rendering in augmented reality and virtual reality applications, where accurate depiction of reflective surfaces is crucial. The method's capacity for reflection manipulation at the pixel level also hints at potential applications in interactive scene editing and real-time simulations.

Theoretical implications include advancing the understanding of disentangled representations in neural rendering frameworks. RefGaussian's bilateral smoothness constraint integrates photometric consistency and segmentation, presenting a novel approach to joint optimization in scene decomposition.

Future work may explore the extension of RefGaussian to more complex scenarios involving irregular surfaces. Enhancements could also focus on refining the granularity of reflection manipulation, enabling more precise and flexible control over rendered reflections.

Conclusion

RefGaussian represents a significant advancement in the domain of neural rendering, addressing critical limitations in reflection modeling within 3D Gaussian Splatting. Through its innovative decomposition and synchronous rendering paradigm, it achieves impressive results in both realistic scene reconstruction and rendering efficiency. The method's contributions lay the foundation for further research and development in highly realistic and interactive rendering applications.