RHINO: Regularizing the Hash-based Implicit Neural Representation
This work addresses a specific bottleneck in hash-based implicit neural representations for researchers and practitioners in computer vision and graphics, offering an incremental improvement to enhance regularization without modifying existing architectures.
The paper tackled the problem of insufficient regularization in hash-based implicit neural representations, which leads to unreliable and noisy interpolation results, by introducing RHINO, a method that incorporates a continuous analytical function to improve gradient flow and regularization, achieving superior quality and speed over state-of-the-art techniques in tasks like image fitting and neural radiance fields.
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.