# OmniField: Conditioned Neural Fields for Robust Multimodal Spatiotemporal Learning

Source: [OmniField: Conditioned Neural Fields for Robust Multimodal Spatiotemporal Learning](<https://www.sota2.com/research/paper/690ad9ee5fbd5403840045ab>)

Collection index: [llms.txt](<https://www.sota2.com/research/llms.txt>)

Data updated: 2025-11-06T05:03:43.312Z

Authors: Kevin Valencia, Thilina Balasooriya, Xihaier Luo, Shinjae Yoo, David Keetae Park

Published: 2025-11-04T02:50:21.000Z

## 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.

## Recorded benchmark results

A research result applies to its stated dataset, split, metric and evaluation configuration. Compare like-for-like settings, preserve the metric's unit and direction, and distinguish paper-reported results from independent reproduction. The best result recorded here is not a claim of exhaustive, current global coverage.

- [Spatiotemporal Forecasting on AirDelhi AD-B](<https://www.sota2.com/research/sota/spatiotemporal-forecasting-on-airdelhi-ad-b.md>): Spatiotemporal forecasting; AirDelhi AD-B; CRPS: 29.244.

- [Spatiotemporal Field Reconstruction on Navier-Stokes (Full)](<https://www.sota2.com/research/sota/spatiotemporal-field-reconstruction-on-navier-stokes-full.md>): Spatiotemporal Field Reconstruction; Navier-Stokes (Full); CRPS: 0.4765.

- [Spatiotemporal Field Reconstruction on Navier-Stokes 10% Subset](<https://www.sota2.com/research/sota/spatiotemporal-field-reconstruction-on-navier-stokes-10-percent-subset.md>): Spatiotemporal Field Reconstruction; Navier-Stokes 10% Subset; CRPS: 1.0651.

## Original sources and implementation

[Original paper](<https://arxiv.org/abs/2511.02205>)

[PDF](<https://arxiv.org/pdf/2511.02205.pdf>)