Scaffold-GS: Structured 3D Gaussians for View-Adaptive Rendering (2312.00109v1)

Abstract: Neural rendering methods have significantly advanced photo-realistic 3D scene rendering in various academic and industrial applications. The recent 3D Gaussian Splatting method has achieved the state-of-the-art rendering quality and speed combining the benefits of both primitive-based representations and volumetric representations. However, it often leads to heavily redundant Gaussians that try to fit every training view, neglecting the underlying scene geometry. Consequently, the resulting model becomes less robust to significant view changes, texture-less area and lighting effects. We introduce Scaffold-GS, which uses anchor points to distribute local 3D Gaussians, and predicts their attributes on-the-fly based on viewing direction and distance within the view frustum. Anchor growing and pruning strategies are developed based on the importance of neural Gaussians to reliably improve the scene coverage. We show that our method effectively reduces redundant Gaussians while delivering high-quality rendering. We also demonstrates an enhanced capability to accommodate scenes with varying levels-of-detail and view-dependent observations, without sacrificing the rendering speed.

• The paper presents a novel structured 3D Gaussian framework that leverages anchor points and a dual-layer hierarchy for dynamic, view-adaptive rendering.
• It introduces efficient anchor growing and pruning strategies to reduce redundancy and achieve near 100 FPS rendering at 1K resolution.
• Experimental results validate Scaffold-GS across diverse datasets, demonstrating superior rendering quality and reduced storage compared to traditional methods.

Enhancing Neural 3D Scene Representations with Scaffold-GS

Introduction to Scaffold-GS

The evolution of photo-realistic rendering of 3D scenes has been a pivotal area of focus in computer graphics and computer vision research. With applications stretching from virtual reality to large-scale scene visualization, the quest for efficient and high-quality rendering methods remains imperative. The recent work on Scaffold-GS, presented by Tao Lu and colleagues, introduces a novel approach to structured 3D Gaussian representations for dynamic, view-adaptive rendering. This method leverages the principles of 3D Gaussian Splatting (3D-GS) while addressing its limitations in handling scene geometry and redundant Gaussians through the introduction of anchor points and a dual-layer hierarchy.

The Limitations of Existing Methods

Prior approaches to neural rendering oscillate between primitive-based representations and volumetric representations, each bearing its set of drawbacks ranging from low-quality renderings to computational inefficiencies. Notably, the 3D-GS method, despite its advancements, suffers from limitations due to its redundancy in Gaussian representations and lack of robustness against significant view changes and lighting effects. Furthermore, the existing techniques often fall short in efficiently rendering large-scale and complex scenes.

Scaffold-GS Approach

The innovation of Scaffold-GS lies in its strategic distribution of 3D Gaussians anchored on a sparse grid of initial points derived from Structure from Motion (SfM). This structured approach not only enhances the scene representation but also dynamically adapts to varying viewing angles and distances. The key advancements introduced in Scaffold-GS include:

• Anchor Points: A grid of anchor points, initialized from SfM point clouds, sculpts the local scene occupancy. Each anchor associates with neural Gaussians, whose attributes are predicted on-the-fly, thus accommodating diverse viewing conditions.
• Hierarchical Representation: Scaffold-GS forms a hierarchical scene representation, leveraging multi-resolution features and view-dependent weights to capture varying scene granularities.
• Efficient Rendering: Through a series of strategic filtering operations based on the view frustum and opacity values, Scaffold-GS manages to render scenes in real-time, approximately maintaining 100 FPS at 1K resolution, with reduced computational overhead.
• Anchor Refinement: A novel contribution is the anchor growing and pruning strategies, which bolster the method's capability to cover more extensive scene details and improve fidelity, especially in less observed and texture-less regions.

Experimental Insights and Implications

Scaffold-GS's experimental validation across multiple datasets underlines its effectiveness in rendering large outdoor scenes and intricate indoor environments. Comparison with existing methods, including the original 3D-GS, demonstrates superior or comparable rendering quality with significantly reduced storage requirements. This efficiency hints at Scaffold-GS's potential applicability in a wide array of rendering tasks, surpassing current limitations posed by scene complexity and scale.

Future Directions

While Scaffold-GS marks a significant step forward, the dependency on initial SfM points for anchor initialization points to areas ripe for further exploration. Enhancements to the initial anchoring process could pave the way for improved fidelity across even more challenging scenarios. Furthermore, the structured nature of Scaffold-GS opens opportunities for integration with other neural rendering techniques and applications beyond static scene rendering, such as dynamic scene visualization and interactive media generation.

In conclusion, Scaffold-GS redefines the boundaries of neural 3D scene representations, promising advancements in the quality and efficiency of photo-realistic rendering. Its novel use of structured 3D Gaussians, coupled with dynamic view adaptability, heralds new possibilities for future research and application in the field of computer graphics and vision.