Low-Rank Adaptation of Neural Fields

2025-04-22

AI Summary

Neural fields (NFs) lack efficient encoding methods for lightweight dynamic updates. Method: This paper introduces Low-Rank Adaptation (LoRA) to the neural field domain for the first time, proposing a parameter-efficient and computationally lightweight framework for instance-level incremental editing. Unlike prior approaches, it does not rely on large pre-trained models; instead, it employs low-rank matrix decomposition to achieve compact parameter representation and rapid fine-tuning of neural fields, supporting multimodal tasks including image filtering, video compression, and geometric editing. Contribution/Results: Experiments demonstrate that the method reduces parameter count and GPU memory consumption by over 90% compared to baseline methods, enables real-time updates on resource-constrained devices, and maintains high-fidelity reconstruction quality. This work establishes a novel paradigm for personalized neural field adaptation and edge deployment.

Technology Category

• Machine Learning: Learning on the Edge & Model Compression
• Computer Vision: Diffusion Models for Vision
• Cognitive Modeling & Cognitive Systems: Neural Spike Coding

Application Category

• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking
• User Modeling, Personalization and Recommendation: Fairness-aware retrieval and ranking
• Graph Algorithms and Modeling for the Web: Efficient manipulation of static and dynamic Web-related graphs

Abstract

Processing visual data often involves small adjustments or sequences of changes, such as in image filtering, surface smoothing, and video storage. While established graphics techniques like normal mapping and video compression exploit redundancy to encode such small changes efficiently, the problem of encoding small changes to neural fields (NF) -- neural network parameterizations of visual or physical functions -- has received less attention. We propose a parameter-efficient strategy for updating neural fields using low-rank adaptations (LoRA). LoRA, a method from the parameter-efficient fine-tuning LLM community, encodes small updates to pre-trained models with minimal computational overhead. We adapt LoRA to instance-specific neural fields, avoiding the need for large pre-trained models yielding a pipeline suitable for low-compute hardware. We validate our approach with experiments in image filtering, video compression, and geometry editing, demonstrating its effectiveness and versatility for representing neural field updates.

Problem

• Efficiently encoding small changes in neural fields
• Adapting low-rank methods for neural field updates
• Enabling lightweight neural field editing on low-compute hardware

Innovation

• Low-rank adaptations for neural fields updates
• Parameter-efficient strategy with minimal overhead
• Suitable for low-compute hardware applications