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Hierarchical Spatial-Angular Representation Learning for Point-Supervised Salient Object Detection in Light Fields
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DOI：10.1145/3788871.png)
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Abstract
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Light Field Salient Object Detection (LFSOD) aims to identify visually distinctive regions by leveraging the complementary spatial-angular information inherent in 4D light field imagery. A major challenge lies in modeling angular dependencies and maintaining spatial coherence under sparse supervision. In this article, we propose a weakly supervised network that consists of three interdependent modules. First, the Light Field Division (LFD) module utilizes epipolar geometry to extract direction-aware boundary features, enhancing the encoding of angular disparities. Second, the Light Field Spatial Association (LFSA) module anchors cross-view feature alignment using central-viewpoint annotations, thereby enforcing spatial consistency and mitigating redundant representations. Third, the Light Field Saliency Local Clustering (LFLC) module introduces a joint boundary-appearance modeling strategy that integrates adaptive clustering with error-aware regularization to refine structural predictions. Experiments on three benchmark datasets show that our method consistently outperforms mainstream weakly supervised approaches. It also achieves superior performance compared to several fully supervised methods.
Keywords:
Light Field
Salient Object Detection
Weakly Supervised Learning
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