GSDF: 3DGS Meets SDF for Improved Rendering and Reconstruction
(2403.16964)Abstract
Presenting a 3D scene from multiview images remains a core and long-standing challenge in computer vision and computer graphics. Two main requirements lie in rendering and reconstruction. Notably, SOTA rendering quality is usually achieved with neural volumetric rendering techniques, which rely on aggregated point/primitive-wise color and neglect the underlying scene geometry. Learning of neural implicit surfaces is sparked from the success of neural rendering. Current works either constrain the distribution of density fields or the shape of primitives, resulting in degraded rendering quality and flaws on the learned scene surfaces. The efficacy of such methods is limited by the inherent constraints of the chosen neural representation, which struggles to capture fine surface details, especially for larger, more intricate scenes. To address these issues, we introduce GSDF, a novel dual-branch architecture that combines the benefits of a flexible and efficient 3D Gaussian Splatting (3DGS) representation with neural Signed Distance Fields (SDF). The core idea is to leverage and enhance the strengths of each branch while alleviating their limitation through mutual guidance and joint supervision. We show on diverse scenes that our design unlocks the potential for more accurate and detailed surface reconstructions, and at the meantime benefits 3DGS rendering with structures that are more aligned with the underlying geometry.
Overview
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Introduces GSDF, a novel architecture combining 3D Gaussian Splatting and neural Signed Distance Fields for enhanced scene rendering and reconstruction.
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Features a dual-branch architecture facilitating mutual guidance and joint supervision, significantly improving rendering fidelity and reconstruction quality.
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Demonstrates superior results over existing methods in rendering texture-less areas and intricate geometries, alongside more accurate surface constructions.
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Speculates on future advancements, highlighting the potential of the dual-branch strategy for applications in augmented/virtual reality, robotics, and simulations.
GSDF: Bridging 3D Gaussian Splatting and Neural SDF for Enhanced Scene Rendering and Reconstruction
Introduction to GSDF
In the domain of computer vision and computer graphics, presenting 3D scenes using multiview images is a fundamental yet challenging task, necessitating high-quality rendering and accurate reconstruction. Recent developments in neural volumetric rendering and neural implicit surfaces have significantly advanced the field. However, existing methods often face limitations in rendering fidelity and reconstruction quality due to their inherent constraints. Addressing these challenges, this paper introduces GSDF (Gaussian Splatting and Signed Distance Fields), a novel dual-branch architecture that synergizes the advantages of 3D Gaussian Splatting (3DGS) and neural Signed Distance Fields (SDF). This integration aims to enhance both rendering and reconstruction capabilities by leveraging mutual guidance and joint supervision.
Core Contributions
- Dual-branch Architecture: GSDF introduces a pioneering dual-branch framework consisting of a GS-branch for rendering and an SDF-branch for surface reconstruction, leveraging the benefits of 3DGS and neural SDF simultaneously.
- Mutual Guidance Strategy: The paper presents a method by which each branch enhances the other through depth guided ray sampling, geometry-aware Gaussian density control, and mutual geometry supervision. This synergy resolves the primary limitations associated with each method when used in isolation.
- Significant Quality Improvements: Empirical evaluations demonstrate that GSDF achieves superior results in rendering quality and reconstruction accuracy compared to state-of-the-art methods. The model shows remarkable fidelity in rendering texture-less regions and intricate geometries while providing more accurate and detailed surface reconstructions.
Methodology Overview
GSDF harmonizes the rendering strengths of 3DGS and the geometric accuracy of neural SDFs through a cohesive framework:
- GS $\rightarrow$ SDF: The method utilizes rendered depth maps from the GS-branch to guide the ray sampling process in the SDF-branch. This process effectively steers the optimization of the SDF-branch, leading to accelerated convergence and enhanced geometric detail capture.
- SDF $\rightarrow$ GS: A geometry-aware Gaussian control mechanism is introduced, whereby the distribution and pruning of Gaussian primitives are guided by the SDF values, promoting a more surface-aligned distribution of Gordian primitives.
- GS $\leftrightarrow$ SDF: Mutual geometry supervision encourages coherence in the depth and normal maps estimated from both branches, ensuring structural integrity between the rendered images and reconstructed surfaces.
Experimental Validation
Extensive evaluations across diverse scenes reveal that GSDF not only preserves but also enhances the qualities of both 3DGS rendering and neural surface reconstruction. This is evidenced by structured primitives more closely aligned to the surface, reduced floaters in rendered views, accelerated optimization convergence for the SDF-branch, and notably superior geometry accuracy.
Implications and Speculations on Future Developments
The GSDF framework not only addresses current challenges in neural scene rendering and reconstruction but also opens up pathways for future advancements. The paper speculates that incorporating more sophisticated models for either branch could further push the boundaries of rendering quality and reconstruction accuracy. Additionally, the dual-branch strategy presents potential applications in domains requiring high-fidelity rendering and accurate geometry, such as augmented and virtual reality, robotics, and physical simulations.
In summary, the GSDF framework stands as a significant advancement in the synthesis of neural rendering and implicit surface reconstruction techniques. By effectively marrying 3DGS and SDF, the method sets a new benchmark for rendering quality and reconstruction accuracy, holding promising implications for both theoretical exploration and practical applications in computer graphics and vision.
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