OmniField: Conditioned Neural Fields for Robust Multimodal Spatiotemporal Learning

• URL: http://arxiv.org/abs/2511.02205v1
• Date: Tue, 04 Nov 2025 02:50:21 GMT
• Title: OmniField: Conditioned Neural Fields for Robust Multimodal Spatiotemporal Learning
• Authors: Kevin Valencia, Thilina Balasooriya, Xihaier Luo, Shinjae Yoo, David Keetae Park,
• Abstract summary: We propose a continuity-aware framework that learns a continuous neural field conditioned on available modalities and iteratively fuses cross-modal context.<n>Extensive evaluations show that OmniField consistently outperforms eight strong evaluations prior to multimodaltemporal baselines.
• Score: 14.553753196647241
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Multimodal spatiotemporal learning on real-world experimental data is constrained by two challenges: within-modality measurements are sparse, irregular, and noisy (QA/QC artifacts) but cross-modally correlated; the set of available modalities varies across space and time, shrinking the usable record unless models can adapt to arbitrary subsets at train and test time. We propose OmniField, a continuity-aware framework that learns a continuous neural field conditioned on available modalities and iteratively fuses cross-modal context. A multimodal crosstalk block architecture paired with iterative cross-modal refinement aligns signals prior to the decoder, enabling unified reconstruction, interpolation, forecasting, and cross-modal prediction without gridding or surrogate preprocessing. Extensive evaluations show that OmniField consistently outperforms eight strong multimodal spatiotemporal baselines. Under heavy simulated sensor noise, performance remains close to clean-input levels, highlighting robustness to corrupted measurements.

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