DHGS: Decoupled Hybrid Gaussian Splatting for Driving Scene (2407.16600v3)

Abstract: Existing Gaussian splatting methods often fall short in achieving satisfactory novel view synthesis in driving scenes, primarily due to the absence of crafty designs and geometric constraints for the involved elements. This paper introduces a novel neural rendering method termed Decoupled Hybrid Gaussian Splatting (DHGS), targeting at promoting the rendering quality of novel view synthesis for static driving scenes. The novelty of this work lies in the decoupled and hybrid pixel-level blender for road and non-road layers, without the conventional unified differentiable rendering logic for the entire scene. Still, consistency and continuity in superimposition are preserved through the proposed depth-ordered hybrid rendering strategy. Additionally, an implicit road representation comprised of a Signed Distance Function (SDF) is trained to supervise the road surface with subtle geometric attributes. Accompanied by the use of auxiliary transmittance loss and consistency loss, novel images with imperceptible boundary and elevated fidelity are ultimately obtained. Substantial experiments on the Waymo dataset prove that DHGS outperforms the state-of-the-art methods. The project page where more video evidences are given is: https://ironbrotherstyle.github.io/dhgs_web.

• The paper decouples road and non-road elements using dual Gaussian models for targeted optimization in driving scenes.
• It employs a depth-ordered rendering strategy with SDF-based road regularization to ensure image continuity and detail fidelity.
• Experimental results on the Waymo dataset demonstrate DHGS's superior performance in novel view synthesis against state-of-the-art methods.

A Review of "DHGS: Decoupled Hybrid Gaussian Splatting for Driving Scene"

The paper "DHGS: Decoupled Hybrid Gaussian Splatting for Driving Scene" presents a novel approach to improving the rendering quality of novel view synthesis in driving scenes. The research addresses limitations in existing Gaussian splatting methods, particularly their struggle with rendering complex driving scenarios due to a lack of dedicated geometric constraints and efficient design. The proposed methodology introduces Decoupled Hybrid Gaussian Splatting (DHGS), offering a comprehensive solution that enhances both fidelity and geometric consistency of synthesized images in driving environments.

Key Contributions

1. Decoupling of Scene Elements: The novel decoupling strategy differentiates between road and non-road elements within a driving scene, allowing for targeted optimization of distinct scene components. This decoupling facilitates the application of a depth-ordered rendering strategy that combines two separate Gaussian models—one for road surfaces and another for the surrounding environment.
2. Integration of Depth-Ordered Rendering: By employing a depth-ordered rendering mechanism, DHGS hierarchically superimposes road and non-road Gaussian models, which is crucial for maintaining image continuity and enhancing the accuracy of synthesized novel views.
3. Road Surface Regularization with SDF: The authors implement an implicit road representation using a Signed Distance Field (SDF), which guides the geometric learning of road models with precision. This surface regularization ensures subtle geometric details are captured, contributing to the high fidelity of road elements in the synthesized views.
4. Loss Strategies for Improved Consistency: In addition to traditional training losses, the paper introduces auxiliary transmittance and consistency losses. These are designed to regulate the blending process between road and non-road models, suppressing artifacts and ensuring spatial continuity across rendered images.

Experimental Evaluation

The authors conduct extensive experiments using the Waymo dataset, a comprehensive benchmark for autonomous driving research. DHGS demonstrates superior performance in both quantitative and qualitative evaluations against state-of-the-art methods such as 3DGS, 2DGS, Gaussian Pro, and Scaffold GS. Metrics like PSNR, SSIM, LPIPS, and FID consistently favor DHGS, highlighting its effectiveness in scene reconstruction and novel view synthesis. Notably, DHGS excels in free-view novel view synthesis, a challenging test of a model's capacity to generalize beyond training perspectives.

Practical and Theoretical Implications

With the inclusion of SDF-based supervision, DHGS provides a framework that not only improves the quality of visual scene reconstruction but can also inform downstream tasks in autonomous vehicle perception and planning. This method aids in generating training data that enhances system robustness, particularly in edge-case scenarios involving complex road geometries and subtle environmental features. Theoretically, the integration of decoupling and hierarchy in Gaussian splatting models sets a precedent for future research efforts aiming to improve computational efficiency and rendering accuracy in large-scale outdoor environments.

Future Directions

The paper opens several avenues for future research. One potential direction involves optimizing the decoupling and rendering processes for real-time applications, crucial for deployment in live autonomous driving systems. Further exploration of the integration between learned and pre-defined geometric constraints could yield even richer scene representations, reducing reliance on dense point clouds or extensive manual scene labeling. Finally, adapting DHGS to a broader range of complex urban environments and weather conditions could significantly enhance its practical applicability.

In summary, DHGS presents a compelling advancement in novel view synthesis for driving scenes, demonstrating robust performance across a range of tasks and settings. Its innovative use of decoupled Gaussian models and strategic rendering approaches significantly contributes to the field of autonomous driving and larger 3D scene reconstruction efforts.