On the Effectiveness of Weight-Encoded Neural Implicit 3D Shapes

• URL: http://arxiv.org/abs/2009.09808v3
• Date: Sun, 17 Jan 2021 21:27:01 GMT
• Title: On the Effectiveness of Weight-Encoded Neural Implicit 3D Shapes
• Authors: Thomas Davies and Derek Nowrouzezahrai and Alec Jacobson
• Abstract summary: A neural implicit outputs a number indicating whether the given query point in space is inside, outside, or on a surface. Prior works have focused on _latent-encoded_ neural implicits, where a latent vector encoding of a specific shape is also fed as input. A _weight-encoded_ neural implicit may forgo the latent vector and focus reconstruction accuracy on the details of a single shape.
• Score: 38.13954772608884
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: A neural implicit outputs a number indicating whether the given query point in space is inside, outside, or on a surface. Many prior works have focused on _latent-encoded_ neural implicits, where a latent vector encoding of a specific shape is also fed as input. While affording latent-space interpolation, this comes at the cost of reconstruction accuracy for any _single_ shape. Training a specific network for each 3D shape, a _weight-encoded_ neural implicit may forgo the latent vector and focus reconstruction accuracy on the details of a single shape. While previously considered as an intermediary representation for 3D scanning tasks or as a toy-problem leading up to latent-encoding tasks, weight-encoded neural implicits have not yet been taken seriously as a 3D shape representation. In this paper, we establish that weight-encoded neural implicits meet the criteria of a first-class 3D shape representation. We introduce a suite of technical contributions to improve reconstruction accuracy, convergence, and robustness when learning the signed distance field induced by a polygonal mesh -- the _de facto_ standard representation. Viewed as a lossy compression, our conversion outperforms standard techniques from geometry processing. Compared to previous latent- and weight-encoded neural implicits we demonstrate superior robustness, scalability, and performance.

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