Gradient-domain rendering estimates image-space gradients using correlated sampling, which can be combined with color information to reconstruct smoother and less noisy images. While simple ℒ 2 reconstruction is unbiased, it often leads to visible artifacts. In contrast, most recent reconstruction methods based on learned or handcrafted techniques improve visual quality but introduce bias, leaving the development of practically unbiased reconstruction approaches relatively underexplored. In this work, we propose a generalized framework for unbiased reconstruction in gradient-domain rendering. We first derive the unbiasedness condition under a general formulation that linearly combines pixel colors and gradients. Based on this unbiasedness condition, we design a practical algorithm 1 that minimizes image variance while strictly satisfying unbiasedness. Experimental results demonstrate that our method not only guarantees unbiasedness but also achieves superior quality compared to existing unbiased and slightly biased reconstruction methods.

The advancements in hardware have drawn more attention than ever to high-quality offline rendering with modern stream processors, both in the industry and in research fields. However, the graphics APIs are fragmented and existing shading languages lack high-level constructs such as polymorphism, which adds complexity to developing and maintaining cross-platform high-performance renderers. We present LuisaRender 1 , a high-performance rendering framework for modern stream-architecture hardware. Our main contribution is an expressive C++-embedded DSL for kernel programming with JIT code generation and compilation. We also implement a unified runtime layer with resource wrappers and an optimized Monte Carlo renderer. Experiments on test scenes show that LuisaRender achieves much higher performance than existing research renderers on modern graphics hardware, e.g., 5--11× faster than PBRT-v4 and 4--16× faster than Mitsuba 3.