In this work, we present and study a generalized family of differentiable renderers. We discuss from scratch which components are necessary for differentiable rendering and formalize the requirements for each component. We instantiate our general differentiable renderer, which generalizes existing differentiable renderers like SoftRas and DIB-R, with an array of different smoothing distributions to cover a large spectrum of reasonable settings. We evaluate an array of differentiable renderer instantiations on the popular ShapeNet 3D reconstruction benchmark and analyze the implications of our results. Surprisingly, the simple uniform distribution yields the best overall results when averaged over 13 classes; in general, however, the optimal choice of distribution heavily depends on the task.
翻译:在这项工作中,我们介绍并研究一个由不同变种组成的普遍大家庭。我们从头开始讨论哪些组成部分是不同构件的必要组成部分,以形成和正式确定每个构件的要求。我们即时考虑我们的一般变种,它概括了现有的可不同变种,如SoftRas和DIB-R, 包含一系列不同的平滑分布,以涵盖广泛的合理环境。我们评估了流行的 ShapeNet 3D 重建基准的一系列不同变种即时反应,并分析了我们结果的影响。令人惊讶的是,简单的统一分配在平均超过13个等级时产生最佳的总体结果;然而,一般而言,最佳分配选择在很大程度上取决于任务。