Sketch and Patch: Efficient 3D Gaussian Representation for Man-Made Scenes

Abstract: 3D Gaussian Splatting (3DGS) has emerged as a promising representation for photorealistic rendering of 3D scenes. However, its high storage requirements pose significant challenges for practical applications. We observe that Gaussians exhibit distinct roles and characteristics that are analogous to traditional artistic techniques -- Like how artists first sketch outlines before filling in broader areas with color, some Gaussians capture high-frequency features like edges and contours; While other Gaussians represent broader, smoother regions, that are analogous to broader brush strokes that add volume and depth to a painting. Based on this observation, we propose a novel hybrid representation that categorizes Gaussians into (i) Sketch Gaussians, which define scene boundaries, and (ii) Patch Gaussians, which cover smooth regions. Sketch Gaussians are efficiently encoded using parametric models, leveraging their geometric coherence, while Patch Gaussians undergo optimized pruning, retraining, and vector quantization to maintain volumetric consistency and storage efficiency. Our comprehensive evaluation across diverse indoor and outdoor scenes demonstrates that this structure-aware approach achieves up to 32.62% improvement in PSNR, 19.12% in SSIM, and 45.41% in LPIPS at equivalent model sizes, and correspondingly, for an indoor scene, our model maintains the visual quality with 2.3% of the original model size.

• The paper introduces a dual-role Gaussian categorization that efficiently captures scene edges and smooth surfaces.
• It employs techniques like RANSAC-based filtering, polynomial regression, and vector quantization to compress 3D models.
• Experimental results show up to 32.62% PSNR, 19.12% SSIM, and 45.41% LPIPS improvements, enabling real-time immersive applications.

Efficient 3D Gaussian Representation for Man-Made Scenes

The paper "Sketch and Patch: Efficient 3D Gaussian Representation for Man-Made Scenes" introduces an innovative approach to address the inefficiencies in the storage and representation of 3D Gaussian Splatting (3DGS) models. The authors aim to tackle the high storage demands of conventional 3DGS models by presenting a hybrid Gaussian representation optimized for man-made scenes, which are characterized by their rich geometric structures such as edges and smooth surfaces.

Methodology

The authors propose a dual-role categorization of Gaussians in 3DGS: Sketch Gaussians and Patch Gaussians. The Sketch Gaussians are designed to capture boundary-defining features, such as edges and contours of the scene. These Gaussians are encoded using parametric models that exploit their geometric coherence, therefore, efficiently summarizing complex high-frequency details with fewer data resources. Alternatively, the Patch Gaussians focus on broader, smoother regions, leveraging optimized pruning, retraining, and vector quantization to ensure volumetric consistency while enhancing storage efficiency.

To extract and encode Sketch Gaussians, the method utilizes line segment detection techniques from image inputs to identify consistent geometric patterns within the 3D scene. By employing radius search and RANSAC-based filtering, the approach robustly categorizes Gaussians aligned with identifiable 3D linear features. The Sketch Gaussians are then encoded using polynomial regression models specific to their attributes, which significantly minimizes storage while retaining sharp geometric detailing.

For the Patch Gaussians, a sophisticated optimization process is executed, including selective pruning and retraining aligned with surrounding Sketch Gaussians, thus fine-tuning their distribution to achieve efficient representation of smooth regions without degrading visual quality. Additionally, vector quantization further compresses these Gaussians, maximizing storage efficiency across the model.

Results and Implications

The proposed method achieves notable storage reduction without sacrificing visual fidelity. The experiment results reveal substantial improvements, with the proposed model yielding up to 32.62% increase in PSNR, 19.12% in SSIM, and a 45.41% in LPIPS compared to other approaches, at similar storage levels. Intriguingly, for certain indoor scenes, the new model configuration retained visual quality with only approximately 2.3% of the original size. These results underline the method's effectiveness in creating high-fidelity, storage-efficient 3D representations.

The hybrid Gaussian representation has significant implications, particularly in extended reality (XR) applications demanding immersive environments with real-time rendering capabilities. By reducing storage overhead while maintaining high-quality scene reconstruction, this method enables more efficient data transmission and real-time rendering, contributing to the evolving field of immersive multimedia. The approach also opens pathways for further structural-aware compression strategies in 3D scene representation, leveraging parametric encoding and retraining techniques specific to scene topologies.

Future Directions

The paper's insights into hybrid Gaussian representation point to several avenues for future research. Extending the methodology to dynamic scenes involving moving objects could further enhance its applicability in real-time systems. Integrating semantic scene understanding into Gaussian categorization could also yield better representations by prioritizing important scene elements. Additionally, the compatibility of these representations with layered adaptive streaming strategies can be explored to maximize efficiency in bandwidth-constrained environments.

In conclusion, the proposed hybrid Gaussian representation marks a meaningful step towards addressing the storage-efficiency trade-offs in modern 3D scene representation, aligning with industry trends towards more scalable and adaptable immersive systems.