$Δ$-NeRF: Incremental Refinement of Neural Radiance Fields through Residual Control and Knowledge Transfer

2025-11-25

AI Summary

Existing NeRF frameworks struggle with incremental novel-view synthesis, particularly in dynamic scenarios—such as satellite remote sensing—where historical data cannot be revisited, leading to catastrophic forgetting. To address this, we propose Δ-NeRF, a continual optimization framework for neural radiance fields that operates without access to past training data. Its core innovations include a residual controller, an uncertainty-aware gating mechanism, and a view-adaptive selection strategy, enabling modular, forgetting-free incremental refinement. Furthermore, by integrating knowledge distillation with uncertainty modeling, Δ-NeRF achieves a 80% model compression (i.e., 20% of the original size). Evaluated on satellite imagery, Δ-NeRF matches or surpasses full joint training in reconstruction quality (up to +43.5% PSNR) while reducing training time by 30–42%. To our knowledge, it is the first framework to enable efficient, scalable, and lightweight temporal NeRF incremental learning.

Technology Category

• Machine Learning: Life-Long and Continual Learning
• Computer Vision: Diffusion Models for Vision
• Search and Optimization: Learning to Search

Application Category

• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking
• Graph Algorithms and Modeling for the Web: Graph neural networks and deep learning approaches for Web-related graphs
• Semantics and Knowledge: Methods to enhance, augment, integrate or synergize semantic models such as knowledge graphs and LLMs

Abstract

Neural Radiance Fields (NeRFs) have demonstrated remarkable capabilities in 3D reconstruction and novel view synthesis. However, most existing NeRF frameworks require complete retraining when new views are introduced incrementally, limiting their applicability in domains where data arrives sequentially. This limitation is particularly problematic in satellite-based terrain analysis, where regions are repeatedly observed over time. Incremental refinement of NeRFs remains underexplored, and naive approaches suffer from catastrophic forgetting when past data is unavailable. We propose $Δ$-NeRF, a unique modular residual framework for incremental NeRF refinement. $Δ$-NeRF introduces several novel techniques including: (1) a residual controller that injects per-layer corrections into a frozen base NeRF, enabling refinement without access to past data; (2) an uncertainty-aware gating mechanism that prevents overcorrection by adaptively combining base and refined predictions; and (3) a view selection strategy that reduces training data by up to 47% while maintaining performance. Additionally, we employ knowledge distillation to compress the enhanced model into a compact student network (20% of original size). Experiments on satellite imagery demonstrate that $Δ$-NeRF achieves performance comparable to joint training while reducing training time by 30-42%. $Δ$-NeRF consistently outperforms existing baselines, achieving an improvement of up to 43.5% in PSNR over naive fine-tuning and surpassing joint training on some metrics.

Problem

• Enabling incremental refinement of NeRFs without complete retraining
• Preventing catastrophic forgetting when new sequential data arrives
• Reducing computational costs while maintaining 3D reconstruction performance

Innovation

• Residual controller enables incremental refinement without past data
• Uncertainty-aware gating prevents overcorrection in neural radiance fields
• Knowledge distillation compresses enhanced model to 20% original size