论文标题
代数表面的近似可区分渲染
Approximate Differentiable Rendering with Algebraic Surfaces
论文作者
论文摘要
可区分的渲染器在对象的3D表示与该对象的图像之间提供了直接的数学链接。在这项工作中，我们为紧凑的，可解释的表示形式开发了一个近似可区分的渲染器，我们称之为模糊的metaballs。我们的大约渲染器着重于通过深度图和轮廓渲染形状。它牺牲了为实用程序提供忠诚，生成快速运行时间和可用于解决视力任务的高质量梯度信息。与基于网格的可区分渲染器相比，我们的方法的正向通过速度更快5倍，向后传球速度快30倍。我们方法生成的深度图和轮廓图像在任何地方都平滑且定义。在对可区分渲染器进行姿势估计的评估时，我们表明我们的方法是唯一一种与经典技术相媲美的方法。在Silhouette的形状上，我们的方法仅使用梯度下降和每像素损失，而没有任何替代损失或正则化。这些重建即使在具有分割工件的自然视频序列上也很好地工作。项目页面：https：//leonidk.github.io/fuzzy-metaballs
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: https://leonidk.github.io/fuzzy-metaballs
