Octree-GS: Towards Consistent Real-time Rendering with LOD-Structured 3D Gaussians
(2403.17898)Abstract
The recent 3D Gaussian splatting (3D-GS) has shown remarkable rendering fidelity and efficiency compared to NeRF-based neural scene representations. While demonstrating the potential for real-time rendering, 3D-GS encounters rendering bottlenecks in large scenes with complex details due to an excessive number of Gaussian primitives located within the viewing frustum. This limitation is particularly noticeable in zoom-out views and can lead to inconsistent rendering speeds in scenes with varying details. Moreover, it often struggles to capture the corresponding level of details at different scales with its heuristic density control operation. Inspired by the Level-of-Detail (LOD) techniques, we introduce Octree-GS, featuring an LOD-structured 3D Gaussian approach supporting level-of-detail decomposition for scene representation that contributes to the final rendering results. Our model dynamically selects the appropriate level from the set of multi-resolution anchor points, ensuring consistent rendering performance with adaptive LOD adjustments while maintaining high-fidelity rendering results.
Overview
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The paper introduces 'Octree', a novel approach for enhancing 3D Gaussian Splatting with Level-of-Detail (LOD) structuring to achieve consistent real-time rendering.
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Octree addresses the limitation of handling large scenes by dynamically selecting LODs, reducing the computational load through adaptive anchor Gaussian control.
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It outperforms existing methods like Scaffold-GS and Mip-Splatting by maintaining high rendering speeds and reducing the number of Gaussian primitives needed.
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Future research directions highlight optimizing LOD bias, exploring adaptive LOD strategies for dynamic scenes, and expanding capabilities for more complex scenarios.
Introducing Octree: Enhancing 3D Gaussian Splatting with LOD for Consistent Real-Time Rendering
Introduction to LOD in 3D Gaussian Splatting
Recent advancements in 3D Gaussian Splatting (3D-GS) have heralded new possibilities in achieving real-time rendering with high fidelity. Despite these advancements, the technology encounters limitations in handling large scenes with intricate details due to the excessive rendering load imposed by numerous Gaussian primitives within the viewing frustum. This challenge is particularly acute in zoom-out views, leading to inconsistent rendering speeds across different scenes. Another significant hurdle is the approach's inability to appropriately manage detail levels at varying scales, a limitation rooted in its heuristic density control operation.
Addressing these challenges, this paper introduces 'Octree', a novel approach that adeptly applies Level-of-Detail (LOD) structuring to 3D Gaussian scene representations. Through this methodology, Octree dynamically selects appropriate LODs for scene rendering, ensuring both high-quality output and consistent real-time rendering performance across varying levels of scene complexity and scale.
Methodology Overview
LOD-Structured 3D Gaussians
Octree leverages an LOD-structured 3D Gaussian approach wherein scenes are organized in a hierarchical manner with multi-resolution anchor points. This format allows for efficient adaptation to both complex and large-scale scenes during rendering. A key innovation in Octree is the dynamic selection of LODs based on the observation footprint and content richness, which markedly reduces the computational load by adapting the number of rendered Gaussian primitives according to the scene's requirements.
Adaptive Anchor Gaussian Control
This approach introduces operations for growing and pruning anchor Gaussians across different LOD levels, allowing for an organic adaptation in Gaussian density that matches the detail required by the scene. Specifically, the method employs a progressive training strategy that enhances detail alignment while minimizing unnecessary computations.
Results and Implications
Octree's application to various datasets demonstrates its superior ability to capture fine details in large-scale scenes and navigate through extreme-view sequences with consistency. Notably, it maintains fast rendering speeds often exceeding 30 FPS at high resolutions, outperforming state-of-the-art methods like Scaffold-GS and Mip-Splatting. This is achieved through a significant reduction in the number of Gaussian primitives required for rendering, ensuring both efficiency and high-quality visual output.
Future Directions
While Octree marks a significant advance in real-time rendering with LOD, several areas for further research emerge. These include optimizing the LOD bias for greater detail capture and exploring how adaptive LOD strategies can enhance rendering in dynamic scenes with varying levels of complexity. Additionally, expanding Octree's capabilities to support more complex scene structures and rendering scenarios presents a fruitful avenue for future exploration.
In summary, Octree stands as a promising development in the field of neural scene representations, offering an efficient solution to the challenges of real-time rendering in large and detail-rich environments. Its innovative application of LOD principles to 3D Gaussians paves the way for more immersive and high-quality real-world streaming experiences, highlighting the potential of advanced neural rendering methods in transforming interactive content creation and consumption.
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