Differentiable renderers provide a direct mathematical link between an
object's 3D representation and images of that object. In this work, we develop
an approximate differentiable renderer for a compact, interpretable
representation, which we call Fuzzy Metaballs. Our approximate renderer focuses
on rendering shapes via depth maps and silhouettes. It sacrifices fidelity for
utility, producing fast runtimes and high-quality gradient information that can
be used to solve vision tasks. Compared to mesh-based differentiable renderers,
our method has forward passes that are 5x faster and backwards passes that are
30x faster. The depth maps and silhouette images generated by our method are
smooth and defined everywhere. In our evaluation of differentiable renderers
for pose estimation, we show that our method is the only one comparable to
classic techniques. In shape from silhouette, our method performs well using
only gradient descent and a per-pixel loss, without any surrogate losses or
regularization. These reconstructions work well even on natural video sequences
with segmentation artifacts. Project page:
this https URL

本文提出一种称为 “Fuzzy Metaballs” 的紧凑且可解释的表示形式的近似可微渲染器，该渲染器主要通过深度图和轮廓渲染形状，相对于网格的可微渲染器，我们的方法具有更高的效率和质量，可用于解决视觉任务，评估中我们发现，我们的方法是唯一可以与传统技术媲美的，适用于姿态估计和轮廓重建。