Generalizable NeRF can directly synthesize novel views across new scenes, eliminating the need for scene-specific retraining in vanilla NeRF. A critical enabling factor in these approaches is the extraction of a generalizable 3D representation by aggregating source-view features. In this paper, we propose an Entangled View-Epipolar Information Aggregation method dubbed EVE-NeRF. Different from existing methods that consider cross-view and along-epipolar information independently, EVE-NeRF conducts the view-epipolar feature aggregation in an entangled manner by injecting the scene-invariant appearance continuity and geometry consistency priors to the aggregation process. Our approach effectively mitigates the potential lack of inherent geometric and appearance constraint resulting from one-dimensional interactions, thus further boosting the 3D representation generalizablity. EVE-NeRF attains state-of-the-art performance across various evaluation scenarios. Extensive experiments demonstate that, compared to prevailing single-dimensional aggregation, the entangled network excels in the accuracy of 3D scene geometry and appearance reconstruction. Our code is publicly available at https://github.com/tatakai1/EVENeRF.

翻译：可泛化NeRF能够直接跨新场景合成新视角，消除了原始NeRF中需要针对特定场景重新训练的需求。这类方法的关键实现要素是通过聚合源视角特征提取可泛化的三维表示。本文提出一种名为EVE-NeRF的纠缠视图-极线信息聚合方法。与现有方法将跨视角信息与沿极线信息独立处理不同，EVE-NeRF通过将场景不变的外观连续性和几何一致性先验注入聚合过程，以纠缠方式实现视图-极线特征聚合。该方法有效缓解了单维交互可能导致的固有几何与外观约束缺失问题，从而进一步提升三维表示的泛化能力。EVE-NeRF在多种评估场景中均达到当前最优性能。大量实验表明，与主流的单维聚合方法相比，纠缠网络在三维场景几何与外观重建精度方面表现更优。我们的代码已开源：https://github.com/tatakai1/EVENeRF