Generalizable 3D Gaussian splitting (3DGS) can reconstruct new scenes from sparse-view observations in a feed-forward inference manner, eliminating the need for scene-specific retraining required in conventional 3DGS. However, existing methods rely heavily on epipolar priors, which can be unreliable in complex realworld scenes, particularly in non-overlapping and occluded regions. In this paper, we propose eFreeSplat, an efficient feed-forward 3DGS-based model for generalizable novel view synthesis that operates independently of epipolar line constraints. To enhance multiview feature extraction with 3D perception, we employ a selfsupervised Vision Transformer (ViT) with cross-view completion pre-training on large-scale datasets. Additionally, we introduce an Iterative Cross-view Gaussians Alignment method to ensure consistent depth scales across different views. Our eFreeSplat represents an innovative approach for generalizable novel view synthesis. Different from the existing pure geometry-free methods, eFreeSplat focuses more on achieving epipolar-free feature matching and encoding by providing 3D priors through cross-view pretraining. We evaluate eFreeSplat on wide-baseline novel view synthesis tasks using the RealEstate10K and ACID datasets. Extensive experiments demonstrate that eFreeSplat surpasses state-of-the-art baselines that rely on epipolar priors, achieving superior geometry reconstruction and novel view synthesis quality.
可泛化的三维高斯分裂（3DGS）可以在前向推理中从稀疏视角观察中重建新场景，无需像传统3DGS那样进行场景特定的重新训练。然而，现有方法严重依赖极线先验，这在复杂的真实场景中可能不可靠，尤其在非重叠和遮挡区域中。本文提出了一种高效的前向推理3DGS模型——eFreeSplat，用于泛化的新视角合成，能够独立于极线约束进行操作。为提升多视角特征提取的三维感知能力，我们在大规模数据集上使用自监督的Vision Transformer (ViT)进行跨视角补全预训练。此外，我们引入了一种迭代的跨视角高斯对齐方法，以确保不同视角间的深度尺度一致性。eFreeSplat代表了一种创新的泛化新视角合成方法。与现有的纯无几何方法不同，eFreeSplat更加注重实现无极线约束的特征匹配和编码，通过跨视角预训练提供三维先验。我们在RealEstate10K和ACID数据集上进行了宽基线新视角合成任务的评估。大量实验表明，eFreeSplat在几何重建和新视角合成质量上均超越了依赖极线先验的最新基准方法。