Mixture of Volumetric Primitives for Efficient Neural Rendering (2103.01954v2)

Abstract: Real-time rendering and animation of humans is a core function in games, movies, and telepresence applications. Existing methods have a number of drawbacks we aim to address with our work. Triangle meshes have difficulty modeling thin structures like hair, volumetric representations like Neural Volumes are too low-resolution given a reasonable memory budget, and high-resolution implicit representations like Neural Radiance Fields are too slow for use in real-time applications. We present Mixture of Volumetric Primitives (MVP), a representation for rendering dynamic 3D content that combines the completeness of volumetric representations with the efficiency of primitive-based rendering, e.g., point-based or mesh-based methods. Our approach achieves this by leveraging spatially shared computation with a deconvolutional architecture and by minimizing computation in empty regions of space with volumetric primitives that can move to cover only occupied regions. Our parameterization supports the integration of correspondence and tracking constraints, while being robust to areas where classical tracking fails, such as around thin or translucent structures and areas with large topological variability. MVP is a hybrid that generalizes both volumetric and primitive-based representations. Through a series of extensive experiments we demonstrate that it inherits the strengths of each, while avoiding many of their limitations. We also compare our approach to several state-of-the-art methods and demonstrate that MVP produces superior results in terms of quality and runtime performance.

• The paper introduces the Mixture of Volumetric Primitives (MVP) method to overcome memory and computation challenges in dynamic 3D rendering.
• It leverages a convolutional neural network with a guide mesh and an opacity fade factor to optimize scene reconstruction and runtime performance.
• Experimental results demonstrate that MVP outperforms methods like Neural Volumes and NeRF in quality metrics and real-time rendering speed.

Overview of "Mixture of Volumetric Primitives for Efficient Neural Rendering"

The paper "Mixture of Volumetric Primitives for Efficient Neural Rendering" introduces a novel approach to neural scene representation, aiming to balance the strengths of volumetric and primitive-based methods. The Mixture of Volumetric Primitives (MVP) method is designed to handle dynamic 3D rendering efficiently, offering a significant boost in rendering quality and runtime performance compared to existing methods.

MVP addresses the limitations of previous volumetric methods, such as Neural Volumes and Neural Radiance Fields, which struggled with high memory demands and required extensive computational resources for real-time applications. By integrating the benefits of volumetric and primitive-based approaches, MVP achieves a representation capable of detailed rendering while ensuring efficient computation.

Methodology

MVP represents dynamic 3D scenes with a combination of volumetric primitives that focus computational resources on occupied regions of space, significantly reducing the render cost in unoccupied areas. This representation leverages a convolutional neural network architecture, allowing shared computation across primitives and minimizing redundant calculations. Furthermore, MVP integrates correspondence and tracking constraints, enhancing robustness in areas where traditional tracking methods fail, such as in translucent structures or regions with high topological variability.

The proposed system employs a guide mesh to loosely define the primitive positions, allowing the primitives to dynamically adapt as needed to optimize the reconstruction quality. The paper emphasizes the importance of an opacity fade factor in the training phase, encouraging the primitives to expand into unexplored regions by slowing down gradient propagation at volume edges.

Results and Evaluation

Extensive experiments demonstrate that MVP outperforms state-of-the-art methods regarding rendering quality and computational efficiency. For instance, it provides better detail retention in dynamic scenes and outpaces competitors like Neural Volumes and NeRF in terms of framerate and resolution of the synthesized images. MVP is shown to scale across different numbers of volumetric primitives, allowing it to maintain high quality and performance across various scene complexities.

Quantitative metrics, such as Mean Squared Error (MSE), Peak Signal-to-Noise Ratio (PSNR), and Structural Similarity Index (SSIM), reflect MVP's superior performance, while qualitative comparisons highlight the approach's ability to produce sharp and detailed renderings of complex 3D scenes. The considerable reduction in rendering time—achieving real-time performance on high-end hardware—demonstrates the practical implications of this research in applications like virtual reality and telepresence.

Implications and Future Work

MVP's contributions lay the groundwork for future advancements in efficient neural rendering for dynamic applications. The hybrid approach of integrating volumetric and primitive-based methods presents a promising avenue for further development in 3D rendering, potentially influencing real-time gaming, film, and interactive media industries.

Potential expansions on this work include improving the robustness and self-organizing capability of the volumetric primitives, allowing for even greater adaptability and resolution in complex scenes without reliance on an initial guide mesh. Additionally, optimizing the overlap minimization strategies could further enhance rendering speed and reduce computational costs, promoting broader applicability in real-world scenarios.

In summary, the MVP model is a significant contribution to the field of neural rendering, offering a scalable, efficient, and high-quality solution for dynamic scene synthesis that balances detail fidelity with computational pragmatism.