Neural Gaussian Scale-Space Fields

• URL: http://arxiv.org/abs/2405.20980v1
• Date: Fri, 31 May 2024 16:26:08 GMT
• Title: Neural Gaussian Scale-Space Fields
• Authors: Felix Mujkanovic, Ntumba Elie Nsampi, Christian Theobalt, Hans-Peter Seidel, Thomas Leimkühler,
• Abstract summary: We present an efficient method to learn the continuous, anisotropic Gaussian scale space of an arbitrary signal. Our approach is trained self-supervised, i.e., training does not require any manual filtering. Our neural Gaussian scale-space fields faithfully capture multiscale representations across a broad range of modalities.
• Score: 60.668800252986976
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Gaussian scale spaces are a cornerstone of signal representation and processing, with applications in filtering, multiscale analysis, anti-aliasing, and many more. However, obtaining such a scale space is costly and cumbersome, in particular for continuous representations such as neural fields. We present an efficient and lightweight method to learn the fully continuous, anisotropic Gaussian scale space of an arbitrary signal. Based on Fourier feature modulation and Lipschitz bounding, our approach is trained self-supervised, i.e., training does not require any manual filtering. Our neural Gaussian scale-space fields faithfully capture multiscale representations across a broad range of modalities, and support a diverse set of applications. These include images, geometry, light-stage data, texture anti-aliasing, and multiscale optimization.

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