Modular Primitives for High-Performance Differentiable Rendering (2011.03277v1)

Abstract: We present a modular differentiable renderer design that yields performance superior to previous methods by leveraging existing, highly optimized hardware graphics pipelines. Our design supports all crucial operations in a modern graphics pipeline: rasterizing large numbers of triangles, attribute interpolation, filtered texture lookups, as well as user-programmable shading and geometry processing, all in high resolutions. Our modular primitives allow custom, high-performance graphics pipelines to be built directly within automatic differentiation frameworks such as PyTorch or TensorFlow. As a motivating application, we formulate facial performance capture as an inverse rendering problem and show that it can be solved efficiently using our tools. Our results indicate that this simple and straightforward approach achieves excellent geometric correspondence between rendered results and reference imagery.

• The paper introduces a modular design that integrates hardware-supported graphics primitives into differentiable rendering frameworks, boosting performance and scalability.
• The paper presents an analytic antialiasing method to compute accurate visibility gradients, essential for learning vertex positions in complex scenes.
• The paper demonstrates that the proposed renderer outperforms existing methods in high-resolution and dynamic applications like facial performance capture.

Modular Primitives for High-Performance Differentiable Rendering: A Summary

This paper introduces a novel approach to differentiable rendering that leverages the capabilities of existing hardware graphics pipelines, providing substantial performance improvements over previous methods. The authors propose a modular primitive-based design that supports essential graphics pipeline operations such as rasterization, attribute interpolation, and filtered texture lookups. This design directly integrates with automatic differentiation frameworks, such as PyTorch and TensorFlow, enabling the construction of high-performance, customizable rendering pipelines within these systems.

Key Contributions

1. Modular Design for Differentiable Rendering: The authors identify and implement four key modular primitives: rasterization, interpolation, texture filtering, and antialiasing. These primitives are designed to leverage the hardware-supported graphics pipeline for efficiency, thereby reducing unnecessary computational overhead. The separation of shading from the rasterization process allows shading to be implemented flexibly using dense tensor operations in host AD libraries, thus promoting scalability and user customization.
2. Efficient Visibility Gradient Computation: A crucial aspect of differentiable rendering is obtaining accurate visibility gradients. The authors employ an analytic antialiasing method to smooth discontinuities at visibility boundaries, allowing for the computation of visibility gradients necessary for learning vertex positions. While the method approximates coverage using axis-aligned slabs, it demonstrates sufficient generality and accuracy for a variety of scenes without requiring complex sampling strategies.
3. Performance and Scalability: The proposed renderer significantly outperforms existing differentiable rasterization methods, such as Soft Rasterizer and PyTorch3D, especially in scenarios with high-resolution outputs and complex geometric scenes. The renderer's performance is relatively insensitive to depth complexity, a desirable property for scaling to large scenes without compromising speed.
4. Applications and Validation: A notable application of the proposed system is in facial performance capture, framed as an inverse rendering problem. The paper demonstrates that the renderer can be used to accurately deduce dynamic facial geometry from multi-view video data. This application highlights the renderer's potential to replace more complex, state-of-the-art commercial systems for certain domains.

Results and Implications

The empirical results validate the proposed system's ability to learn textures and geometry efficiently, even at small resolutions with fine tessellation. The differentiation of filtered texture lookups also allows for accurate recovery of texture details, crucial for many rendering tasks. The performance benchmarks against existing methods indicate substantial efficiency gains, suggesting that the modular design's emphasis on hardware-accelerated operations is a promising direction for differentiable rendering.

Future Prospects

The paper opens several avenues for further exploration. The described system's capabilities suggest potential applications in inverse graphics and 3D reconstruction, potentially integrating more complex shading models and dynamic lighting conditions. Moreover, enhancing the renderer's ability to accurately model non-local shading and interreflections through extensions with path tracing remains an area for future work. Lastly, facilitating broader accessibility through the public release of their system positions it as a valuable tool for the community, promoting innovative approaches in computer vision and AI research.