Approximate Differentiable Rendering with Algebraic Surfaces

• URL: http://arxiv.org/abs/2207.10606v1
• Date: Thu, 21 Jul 2022 16:59:54 GMT
• Title: Approximate Differentiable Rendering with Algebraic Surfaces
• Authors: Leonid Keselman, Martial Hebert
• Abstract summary: Fuzzy Metaballs is an approximate differentiable for a compact, interpretable representation. Our approximate focuses on rendering shapes via depth maps and silhouettes. Compared to mesh-based differentiables, our method has forward passes that are 5x faster and backwards passes that are 30x faster.
• Score: 24.7500811470085
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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

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