Jrender: An efficient differentiable rendering library based on Jittor

Abstract

Differentiable rendering has been proven as a powerful tool to bridge 2D images and 3D models. With the aid of differentiable rendering, tasks in computer vision and computer graphics could be solved more elegantly and accurately. To address challenges in the implementations of differentiable rendering methods, we present an efficient and modular differentiable rendering library named Jrender based on Jittor. Jrender supports surface rendering for 3D meshes and volume rendering for 3D volumes. Compared with previous differentiable renderers, Jrender exhibits a significant improvement in both performance and rendering quality. Due to the modular design, various rendering effects such as PBR materials shading, ambient occlusions, soft shadows, global illumination, and subsurface scattering could be easily supported in Jrender, which are not available in other differentiable rendering libraries. To validate our library, we integrate Jrender into applications such as 3D object reconstruction and NeRF, which show that our implementations could achieve the same quality with higher performance.

Keywords

• Differentiable rendering
• Real-time rendering
• Deep learning