Nvidia Introduces Modular Primitives for High-Performance Differentiable Rendering

Differentiable rendering is a fundamental building block for 3D geometry that enables the gradients of 3D objects to be calculated and propagated through images while also reducing the need for 3D data collection and annotation. In a bid to provide high-performance primitive operations for rasterization-based differentiable rendering, researchers from Nvidia and Aalto University have introduced a modular primitive that uses existing, highly optimized hardware graphics pipelines to deliver performance superior to previous differentiable rendering systems.

The researchers say the proposed modular primitive can implement a more efficient differentiable real-time graphics pipeline, and identify four highlights of the approach:

• Efficiency: Renders 3D scenes that are complex in terms of geometric detail, occlusion, and appearance in high resolution.
• Minimalism: Easily integrates with modern automatic differentiation (AD) frameworks such as PyTorch and Tensorflow.
• Freedom: Supports arbitrary user-specified shading and arbitrary parameterizations of input geometry without committing to specific forms.
• Quality: Supports texture filtering operations with the required mipmap levels generated internally, with no need to make any assumptions about texture content.

The proposed differentiable rendering systems not only yield good performance, they also provide custom, high-performance implementations such as rasterizing large numbers of triangles, attribute interpolation, filtered texture lookups and user-programmable shading and geometry processing, all in high resolution.

To assess the performance of the proposed method, the researchers selected meshes of varying triangle counts from the ShapeNet database, then rendered them using both the proposed method and two benchmarks (Soft Rasterizer and PyTorch3D) at multiple resolutions.

In the experiments, the proposed method required much less rendering and gradients time and also showed better scalability. The results also demonstrate that the new approach can achieve excellent geometric correspondence between rendered results and reference imagery.

The paper Modular Primitives for High-Performance Differentiable Rendering is on arXiv.

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Analyst: Hecate He | Editor: Michael Sarazen; Yuan Yuan

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