Refine Now, Query Fast: A Decoupled Refinement Paradigm for Implicit Neural Fields

TL;DR

The DRR paradigm offers an effective strategy for building powerful and practical neural field surrogates and INRs in broader applications, with a minimal compromise between speed and quality.

Abstract

Implicit Neural Representations (INRs) have emerged as promising surrogates for large 3D scientific simulations due to their ability to continuously model spatial and conditional fields, yet they face a critical fidelity-speed dilemma: deep MLPs suffer from high inference cost, while efficient embedding-based models lack sufficient expressiveness. To resolve this, we propose the Decoupled Representation Refinement (DRR) architectural paradigm. DRR leverages a deep refiner network, alongside non-parametric transformations, in a one-time offline process to encode rich representations into a compact and efficient embedding structure. This approach decouples slow neural networks with high representational capacity from the fast inference path. We introduce DRR-Net, a simple network that validates this paradigm, and a novel data augmentation strategy, Variational Pairs (VP) for improving INRs under complex tasks like high-dimensional surrogate modeling. Experiments on several ensemble simulation datasets demonstrate that our approach achieves state-of-the-art fidelity, while being up to 27$\times$ faster at inference than high-fidelity baselines and remaining competitive with the fastest models. The DRR paradigm offers an effective strategy for building powerful and practical neural field surrogates and \rev{INRs in broader applications}, with a minimal compromise between speed and quality.

Figures (11)

• Figure 1: Our Decoupled Representation Refinement (DRR) paradigm synthesizes the strengths of slow, high-fidelity MLPs (a) and fast, less expressive embedding-based models (b, compared at similar model sizes) as a well-rounded design pattern for fast and accurate INRs.
• Figure 2: Concrete instantiation of the DRR paradigm via DRR-Net. Part (a) illustrates the overall architectural components, while part (b) and (c) detail the unification and refinement procedure applied to the base multi-resolution embedding structures.
• Figure 3: Architecture of the refiner network, which consists of stacked GLU blocks with pre-normalization and a ReLU intermediate activation.
• Figure 4: Visual comparison on an unseen field from the Cloverleaf3D (top) and Nyx (bottom) dataset. Reconstructed $128^3$ points in just 0.05 seconds, our DRR-Net better captures complex high-energy and high-density structures in respective datasets than competing state-of-the-art surrogates, demonstrating a superior balance of fidelity and speed.
• Figure 5: Qualitative comparison on the unstructured MPAS-Ocean dataset with one depth layer extracted. The visualization highlights the effectiveness of the DRR paradigm: while the standard embedding-based Explorable-INR suffers from severe artifacts, our DRR-Net successfully captures fine temperature variations, demonstrating the framework's power to enhance a grid-based backbone on challenging, non-Cartesian data, while maintaining efficiency.