RHINO: Regularizing the Hash-based Implicit Neural Representation

• URL: http://arxiv.org/abs/2309.12642v1
• Date: Fri, 22 Sep 2023 06:20:41 GMT
• Title: RHINO: Regularizing the Hash-based Implicit Neural Representation
• Authors: Hao Zhu, Fengyi Liu, Qi Zhang, Xun Cao, Zhan Ma
• Abstract summary: Implicit Neural Representation (INR) through a hash-table has demonstrated impressive effectiveness and efficiency in characterizing intricate signals. However, current state-of-the-art methods exhibit insufficient regularization, often yielding unreliable and noisy results during radiances. We introduce RHINO, in which a continuous analytical function is incorporated to facilitate regularization. Notably, RHINO outperforms current state-of-the-art techniques in both quality and speed, affirming its superiority.
• Score: 34.467625248206346
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: The use of Implicit Neural Representation (INR) through a hash-table has demonstrated impressive effectiveness and efficiency in characterizing intricate signals. However, current state-of-the-art methods exhibit insufficient regularization, often yielding unreliable and noisy results during interpolations. We find that this issue stems from broken gradient flow between input coordinates and indexed hash-keys, where the chain rule attempts to model discrete hash-keys, rather than the continuous coordinates. To tackle this concern, we introduce RHINO, in which a continuous analytical function is incorporated to facilitate regularization by connecting the input coordinate and the network additionally without modifying the architecture of current hash-based INRs. This connection ensures a seamless backpropagation of gradients from the network's output back to the input coordinates, thereby enhancing regularization. Our experimental results not only showcase the broadened regularization capability across different hash-based INRs like DINER and Instant NGP, but also across a variety of tasks such as image fitting, representation of signed distance functions, and optimization of 5D static / 6D dynamic neural radiance fields. Notably, RHINO outperforms current state-of-the-art techniques in both quality and speed, affirming its superiority.

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