Decoupled and Editable Gaussian Splatting with Deferred Shading for Efficient 3D Scene Reconstruction and Editing

Core Concepts

DeferredGS proposes a decoupled Gaussian splatting representation that enables efficient 3D scene reconstruction, texture and lighting editing, and realistic relighting by applying deferred shading.

Abstract

The paper introduces DeferredGS, a method for decoupling and editing the Gaussian splatting representation using deferred shading. Gaussian splatting models a 3D scene as a set of 3D Gaussians with attributes like position, rotation, scaling, opacity, and spherical harmonic coefficients. However, the original Gaussian splatting representation entangles both texture and lighting information, making separate texture and lighting editing impossible. To address this, DeferredGS defines additional attributes such as texture parameters (diffuse albedo, roughness, specular albedo) and normal direction on Gaussians. It also models the illumination with a learnable environment map. Importantly, DeferredGS applies deferred shading, which computes shading at the pixel level, resulting in more realistic relighting effects compared to previous methods that use forward shading. The key components of DeferredGS are: Normal Field Distillation: DeferredGS jointly trains a NeRF-like network and a Gaussian splatting representation to distill the normal field from the signed distance function onto the Gaussians' learnable normal attributes, enabling more accurate geometry reconstruction. Deferred Shading: Instead of performing forward shading for each Gaussian, DeferredGS rasterizes geometry and texture attributes into buffer maps and computes shading at the pixel level under the illumination of a learnable environment map. This avoids blending artifacts when rendering Gaussians under novel lighting conditions. Editing Capabilities: With the decoupled representation, DeferredGS supports geometry editing by deforming the Gaussians based on a deformed mesh proxy, and texture editing by optimizing the Gaussians' texture attributes to fit both the input edited image and randomly rendered images from different viewpoints. Experiments demonstrate that DeferredGS outperforms previous methods in terms of novel view synthesis, decomposition, and relighting quality.

Stats

The paper does not provide any specific numerical data or statistics in the main text. The quantitative results are presented in the form of evaluation metrics like PSNR, SSIM, LPIPS, and MSE.

Quotes

"To the best of our knowledge, DeferredGS is the first to apply the deferred shading technique to Gaussian splatting, which alleviates blending artifacts of previous methods." "Experiments show that our DeferredGS produces more faithful decomposition and editing results compared to previous methods."

Key Insights Distilled From

DeferredGS: Decoupled and Editable Gaussian Splatting with Deferred Shading

by Tong Wu,Jia-... at arxiv.org 04-16-2024

https://arxiv.org/pdf/2404.09412.pdf

Deeper Inquiries

How can DeferredGS be extended to handle more complex lighting conditions, such as dynamic or spatially-varying illumination

DeferredGS can be extended to handle more complex lighting conditions, such as dynamic or spatially-varying illumination, by incorporating more sophisticated lighting models and techniques. One approach could be to integrate dynamic environment maps that change over time to simulate varying lighting conditions. This would involve updating the environment map representation in real-time based on the dynamic lighting changes in the scene. Additionally, DeferredGS could incorporate advanced shading models that account for spatially-varying illumination effects, such as subsurface scattering, global illumination, or indirect lighting. By enhancing the lighting representation and shading techniques, DeferredGS can adapt to a wider range of lighting scenarios, making it more versatile for handling complex lighting conditions.

What are the potential limitations of the Gaussian splatting representation, and how could future work address these limitations to further improve the quality and flexibility of the reconstruction and editing capabilities

The Gaussian splatting representation, while efficient for real-time rendering and editing, has some limitations that could impact the quality and flexibility of reconstruction and editing capabilities. One limitation is the discrete nature of Gaussian splatting, which may lead to aliasing artifacts and loss of geometric details, especially on complex or highly detailed scenes. Future work could address this limitation by exploring adaptive sampling techniques or hybrid representations that combine Gaussian splatting with other continuous implicit representations to capture fine details more accurately. Another limitation is the potential for noise and artifacts in the reconstructed geometry and textures, especially in scenes with reflective surfaces or complex lighting. Improvements in the optimization process and regularization techniques could help mitigate these issues and enhance the overall quality of reconstruction and editing results in Gaussian splatting.

Given the decoupled representation, how could DeferredGS be leveraged for applications beyond novel view synthesis and editing, such as 3D scene understanding or content creation workflows

With its decoupled representation of geometry, texture, and lighting, DeferredGS can be leveraged for various applications beyond novel view synthesis and editing. One potential application is 3D scene understanding, where the decoupled representation can facilitate tasks such as object recognition, semantic segmentation, and scene classification. By leveraging the individual components of the scene representation, such as geometry for spatial understanding, texture for material recognition, and lighting for context analysis, DeferredGS can enhance the accuracy and robustness of 3D scene understanding algorithms. Additionally, in content creation workflows, DeferredGS can be used for virtual production, interactive storytelling, and immersive experiences by enabling real-time manipulation and visualization of 3D scenes with dynamic lighting and texture editing capabilities. This can streamline the content creation process and empower artists and designers to create compelling virtual environments with greater flexibility and control.