Scene graphs provide structured abstractions for scene understanding, yet they often overfit to spurious correlations, severely hindering out-of-distribution generalization. To address this limitation, we propose CURVE, a causality-inspired framework that integrates variational uncertainty modeling with uncertainty-guided structural regularization to suppress high-variance, environment-specific relations. Specifically, we apply prototype-conditioned debiasing to disentangle invariant interaction dynamics from environment-dependent variations, promoting a sparse and domain-stable topology. Empirically, we evaluate CURVE in zero-shot transfer and low-data sim-to-real adaptation, verifying its ability to learn domain-stable sparse topologies and provide reliable uncertainty estimates to support risk prediction under distribution shifts.

翻译：场景图提供了场景理解的结构化抽象，但它们常常过拟合于虚假相关性，严重阻碍了分布外泛化能力。为应对这一局限，我们提出了CURVE，一个因果启发的框架，它将变分不确定性建模与不确定性引导的结构正则化相结合，以抑制高方差、环境特定的关系。具体而言，我们应用原型条件去偏方法，将不变的交互动态从环境依赖的变化中解耦，从而促进稀疏且领域稳定的拓扑结构。通过实证研究，我们在零样本迁移和低数据模拟到真实适应场景中评估了CURVE，验证了其学习领域稳定稀疏拓扑的能力，并提供了可靠的不确定性估计以支持分布偏移下的风险预测。