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.

Physically correct, noise-free global illumination is crucial in physically-based rendering, but often takes a long time to compute. Recent approaches have exploited sparse sampling and filtering to accelerate this process but still cannot achieve interactive performance. It is partly due to the time-consuming ray sampling even at 1 sample per pixel, and partly because of the complexity of deep neural networks. To address this problem, we propose a novel method to generate plausible single-bounce indirect illumination for dynamic scenes in interactive framerates. In our method, we first compute direct illumination and then use a lightweight neural network to predict screen space indirect illumination. Our neural network is designed explicitly with bilateral convolution layers and takes only essential information as input (direct illumination, surface normals, and 3D positions). Also, our network maintains the coherence between adjacent image frames efficiently without heavy recurrent connections. Compared to state-of-the-art works, our method produces single-bounce indirect illumination of dynamic scenes with higher quality and better temporal coherence and runs at interactive framerates.

Spherical Harmonics (SH) have been proven as a powerful tool for rendering, especially in real-time applications such as Precomputed Radiance Transfer (PRT). Spherical harmonics are orthonormal basis functions and are efficient in computing dot products. However, computations of triple product and multiple product operations are often the bottlenecks that prevent moderately high-frequency use of spherical harmonics. Specifically state-of-the-art methods for accurate SH triple products of order n have a time complexity of O ( n 5 ), which is a heavy burden for most real-time applications. Even worse, a brute-force way to compute k -multiple products would take O ( n 2 k ) time. In this paper, we propose a fast and accurate method for spherical harmonics triple products with the time complexity of only O ( n 3 ), and further extend it for computing k -multiple products with the time complexity of O ( kn 3 + k 2 n 2 log ( kn )). Our key insight is to conduct the triple and multiple products in the Fourier space, in which the multiplications can be performed much more efficiently. To our knowledge, our method is theoretically the fastest for accurate spherical harmonics triple and multiple products. And in practice, we demonstrate the efficiency of our method in rendering applications including mid-frequency relighting and shadow fields.