Gaussian Billboards: Expressive 2D Gaussian Splatting with Textures (2412.12734v1)

Abstract: Gaussian Splatting has recently emerged as the go-to representation for reconstructing and rendering 3D scenes. The transition from 3D to 2D Gaussian primitives has further improved multi-view consistency and surface reconstruction accuracy. In this work we highlight the similarity between 2D Gaussian Splatting (2DGS) and billboards from traditional computer graphics. Both use flat semi-transparent 2D geometry that is positioned, oriented and scaled in 3D space. However 2DGS uses a solid color per splat and an opacity modulated by a Gaussian distribution, where billboards are more expressive, modulating the color with a uv-parameterized texture. We propose to unify these concepts by presenting Gaussian Billboards, a modification of 2DGS to add spatially-varying color achieved using per-splat texture interpolation. The result is a mixture of the two representations, which benefits from both the robust scene optimization power of 2DGS and the expressiveness of texture mapping. We show that our method can improve the sharpness and quality of the scene representation in a wide range of qualitative and quantitative evaluations compared to the original 2DGS implementation.

• The paper proposes Gaussian Billboards, integrating per-primitive textures into 2D Gaussian Splatting to significantly improve detailed texture representation.
• Evaluations show Gaussian Billboards enhance scene reconstruction quality, yielding improved PSNR and SSIM metrics and better visual detail on diverse datasets.
• This method has practical implications for industries like gaming, VR, and visualization by enabling more realistic virtual environments without substantial computational increases.

Exploring Gaussian Billboards for Enhanced Scene Representation

The paper "Gaussian Billboards: Expressive 2D Gaussian Splatting with Textures" introduces a promising advancement in the field of scene representation and rendering, particularly targeting the field of computer graphics. This work builds on the concept of 2D Gaussian Splatting (2DGS), which itself is an evolution of the 3D Gaussian Splatting (3DGS) model. The authors propose an innovative method called Gaussian Billboards, which integrates per-primitive textures into the 2DGS framework to significantly bolster the expressive capability of scene representations.

Key Developments and Contributions

Gaussian Splatting has become a staple method for rendering 3D scenes because of its efficiency and quality. However, traditional 2DGS methods have limitations in expressing detailed surface textures, as they rely on solid color per splat.

The main contributions of the Gaussian Billboards method are:

1. Integration of Per-Primitive Textures: Unlike the traditional 2DGS which employs a uniform color for each splat, Gaussian Billboards incorporate a small per-primitive texture, allowing for spatially-varying colors. This drastically improves the model's ability to represent fine texture details without increasing the number of primitives.
2. Methodological Insights: The paper includes an ablation paper detailing the impact of various hyperparameters on the model's performance. These include the resolution of the color grid and the spatial extent within which the texture is defined. The paper indicates optimal settings that balance computational expense with rendering quality.
3. Evaluation across Tasks: The model is tested across different tasks, including overfitting on single images and 3D scene reconstruction using the NeRF360 dataset and a human face dataset. The results, both qualitative and quantitative, demonstrate improved texture representation capabilities of Gaussian Billboards compared to baseline 2DGS methods.

Numerical Results and Implications

• The paper presents compelling numerical evidence of improved scene reconstruction quality. For instance, the PSNR and SSIM metrics show noticeable enhancement when per-primitive textures are employed. This is particularly evident in scenarios with a fixed number of primitives, as well as in dynamic scenarios where the number of primitives is optimized.
• Experimentation across diverse datasets, such as the NeRF360 and face reconstruction tasks, further validates the method. In scenarios where detailed texture is crucial, such as in foliage or intricate urban scenes, the improvements in visual detail are stark, despite a slight increase in computational overhead.

Theoretical and Practical Implications

From a theoretical standpoint, this modification introduces an enriched framework for scene representation in computer graphics. By decoupling texture information from geometric information, Gaussian Billboards offer a versatile representation model that could inspire further theoretical developments in rendering techniques.

Practically, this methodology could have significant implications for industries relying on detailed 3D rendering, such as video game design, virtual reality, or architectural visualization. The enhanced texture fidelity can translate to more realistic and immersive virtual environments without necessarily escalating the computational footprint.

Future Directions

While the Gaussian Billboards method is poised to substantially impact scene rendering quality, it opens several avenues for future exploration. Enhancing the scalability of the model, particularly concerning the shared memory constraints in CUDA implementations, could be a fruitful direction, as the current limitations cap the resolution of textures to a modest size.

Additionally, integrating more advanced models of reflectance and illumination, such as physically-based shading models, could further optimize the utility of Gaussian Billboards, particularly in scenes with complex lighting conditions.

In conclusion, the introduction of Gaussian Billboards marks a significant enhancement in the representation of detailed textures in 3D scene reconstruction. The approach adeptly balances expressive power with computational efficiency, showcasing its potential as a pivotal tool for developers and researchers in the field of computer graphics.