A light field camera can reconstruct 3D scenes using captured multi-focus images that contain rich spatial geometric information, enhancing applications in stereoscopic photography, virtual reality, and robotic vision. In this work, a state-of-the-art salient object detection model for multi-focus light field images, called LFSamba, is introduced to emphasize four main insights: (a) Efficient feature extraction, where SAM is used to extract modality-aware discriminative features; (b) Inter-slice relation modeling, leveraging Mamba to capture long-range dependencies across multiple focal slices, thus extracting implicit depth cues; (c) Inter-modal relation modeling, utilizing Mamba to integrate all-focus and multi-focus images, enabling mutual enhancement; (d) Weakly supervised learning capability, developing a scribble annotation dataset from an existing pixel-level mask dataset, establishing the first scribble-supervised baseline for light field salient object detection.https://github.com/liuzywen/LFScribble
@article{arxiv.2411.06652,
title = {LFSamba: Marry SAM with Mamba for Light Field Salient Object Detection},
author = {Zhengyi Liu and Longzhen Wang and Xianyong Fang and Zhengzheng Tu and Linbo Wang},
journal= {arXiv preprint arXiv:2411.06652},
year = {2024}
}