Neural fields learn faster with smarter region sampling techniques
Efficient Neural Field Learning via Adaptive Coverage and Focused Sampling
Machine LearningArtificial Intelligence
Summary
Training neural networks to represent complex continuous data like scientific fields can be slow because typical methods sample points evenly across space, missing areas that need more attention. The authors propose ACES, a method that divides the space into adaptive regions and focuses learning on important ones while still covering the whole area. This approach reduces redundant samples in simple regions and better targets complex parts, speeding up training and improving accuracy. Their experiments show ACES works best when the data has areas of localized complexity.
What this means in practice
- •For scientific computing teams: Improve simulation and modeling speed by efficiently training neural networks to represent complex scientific data with focused region sampling.
- •For graphics engine developers: Enhance 3D scene representation by using adaptive region-based sampling to train neural fields more quickly and accurately.
Authors
Guang Zhao, Xihaier Luo, Huan-Hsin Tseng, Seungjun Lee, Shinjae Yoo, Yihui Ren, Wei Xu
Abstract
Implicit neural representations (INRs) provide a flexible framework for modeling high-dimensional continuous fields, but their training is often inefficient due to uniform subsampling that ignores spatial heterogeneity. Existing adaptive sampling methods partially address this issue by prioritizing high-error samples, but typically operate at the point level, often leading to redundant sampling in localized regions and insufficient coverage of the domain. We propose ACES (Adaptive Coverage-aware Efficient Sampling), a structured sampling framework that improves training efficiency by decoupling coverage and importance. ACES constructs adaptive spatial partitions to ensure domain coverage and reduce redundancy, and applies region-level importance weighting to prioritize informative regions during training. We provide a theoretical analysis showing that adaptive partitioning reduces gradient variance by increasing within-region homogeneity, and that controlled bias in region-level weighting may improve optimization efficiency relative to standard unbiased estimators. Experiments on scientific field learning tasks demonstrate that ACES achieves faster convergence and lower error than uniform and pointwise adaptive sampling baselines, with the largest gains in fields with highly localized complexity.