OmniField：用于鲁棒多模态时空学习的条件神经场
OmniField: Conditioned Neural Fields for Robust Multimodal Spatiotemporal Learning
AI总结：
针对多模态时空数据稀疏、噪声及模态缺失挑战，提出OmniField框架，通过条件神经场与迭代跨模态融合实现统一重建与预测，在噪声下表现鲁棒且优于8个基线。
摘要：
现实世界实验数据上的多模态时空学习受限于两大挑战：模态内测量稀疏、不规则且充满噪声（QA/QC伪影），但跨模态间存在相关性；可用模态集随空间和时间变化，除非模型能在训练和测试时适应任意子集，否则可用记录会不断缩减。我们提出了OmniField，这是一个连续性感知框架，它学习以可用模态为条件的连续神经场，并迭代融合跨模态上下文。多模态串扰块架构结合迭代跨模态细化，在解码器之前对齐信号，从而无需网格化或代理预处理，即可实现统一的重建、插值、预测和跨模态预测。广泛评估表明，OmniField始终优于8个强大的多模态时空基线。在严重的模拟传感器噪声下，其性能仍接近无噪声输入水平，凸显了对损坏测量值的鲁棒性。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
