Light field imaging, originated from the availability of light field capture technology, offers a wide range of applications in the field of computational vision. The capability of predicting salient objects of light fields remains technologically challenging due to its complicated geometry structure. In this paper, we propose a light field salient object detection approach that formulates the geometric coherence among multiple views of light fields as graphs, where the angular/central views represent the nodes and their relations compose the edges. The spatial and disparity correlations between multiple views are effectively explored through multi-scale graph neural networks, enabling the more comprehensive understanding of light field content and more representative and discriminative saliency features generation. Moreover, a multi-scale saliency feature consistency learning module is embedded to enhance the saliency features. Finally, an accurate salient object map is produced for the light field based upon the extracted features. In addition, we establish a new light field salient object detection dataset (CITYU-Lytro) that contains 817 light fields with diverse contents and their corresponding annotations, aiming to further promote the research on light field salient object detection. Quantitative and qualitative experiments demonstrate that the proposed method performs favorably compared with the state-of-the-art methods on the benchmark datasets.

中文翻译：

---

通过图神经网络进行光场几何辅助显着物体检测


光场成像起源于光场捕获技术的出现，在计算视觉领域提供了广泛的应用。由于其复杂的几何结构，预测光场显着物体的能力在技术上仍然具有挑战性。在本文中，我们提出了一种光场显着目标检测方法，该方法将光场多个视图之间的几何相干性表示为图形，其中角度/中心视图代表节点，它们的关系构成边缘。通过多尺度图神经网络有效地探索了多个视图之间的空间和视差相关性，从而能够更全面地理解光场内容并生成更具代表性和辨别力的显着特征。此外，嵌入了多尺度显着性特征一致性学习模块以增强显着性特征。最后，根据提取的特征为光场生成准确的显着物体图。此外，我们建立了一个新的光场显着物体检测数据集（CITYU-Lytro），其中包含817个内容丰富的光场及其相应的注释，旨在进一步推动光场显着物体检测的研究。定量和定性实验表明，与基准数据集上最先进的方法相比，所提出的方法表现良好。