发表机构
Simon Fraser University（西蒙菲莎大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
该研究提出灵敏度AOV，作为可微渲染中承载目标对场景参数灵敏度的渲染输出，通过反向模式遍历生成灵敏度缓冲区，旨在将导数输出确立为与原始图像同等的一流渲染产物。
AI 中文摘要
可微渲染器会暴露任意标量目标对每个场景参数的导数，但与原始图像不同——数十年的任意输出变量（AOV）已教会我们对原始图像进行分解、检查和合成——这些导数尚无用于人工检查的成熟表示。我们引入灵敏度AOV，这是一种承载目标对影响它的场景参数灵敏度的渲染输出。单次反向模式遍历会在场景的参数层次结构上填充灵敏度缓冲区，可从中读取多个视图而非重新求导，这与延迟着色直接类似：以对象和参数类型粒度呈现图像空间灵敏度、从任意检查视点投射到可自由导航的场景，以及对于空间变化参数，通过纹理坐标传递到表面的每个纹元场。我们将定义目标的固定相机与用于检查结果的自由相机分离，并将反向模式归因与其正向模式对偶并置。我们的目标不是单一算法，而是建立导数输出作为与原始图像同等地位的一流渲染产物的框架。
英文摘要
Differentiable renderers expose the derivative of any scalar objective with respect to every scene parameter, yet unlike the primal image, which decades of arbitrary output variables (AOVs) have taught us to decompose, inspect, and composite, these derivatives have no established representation for human inspection. We introduce the sensitivity AOV, a render output carrying the sensitivity of an objective to the scene parameters that influence it. A single reverse-mode pass populates a sensitivity buffer over the scene's parameter hierarchy, from which many views are read rather than re-differentiated, in direct analogy to deferred shading: image-space sensitivity at object and parameter-type granularity, projections onto a freely navigable scene from any inspection viewpoint, and, for spatially varying parameters, per-texel fields carried to the surface through texture coordinates. We separate the fixed camera that defines the objective from the free camera used to inspect the result, and position reverse-mode attribution against its forward-mode dual. Our aim is not a single algorithm but a scaffolding that establishes derivative outputs as first-class render products alongside the primal image.
Comments4 pages. SIGGRAPH Asia 2026 Technical Communications
Journal refSIGGRAPH Asia 2026 Technical Communications (SA Technical Communications '26), Kuala Lumpur, Malaysia
