计算机科学
人工智能
光场
计算机视觉
领域(数学)
特征(语言学)
比例(比率)
对角线的
观点
模式识别(心理学)
数学
地理
艺术
语言学
哲学
几何学
地图学
纯数学
视觉艺术
作者
Min Xiao,Chen Lv,Xiaomin Liu
出处
期刊:Sensors
[MDPI AG]
日期:2023-08-28
卷期号:23 (17): 7480-7480
被引量:1
摘要
A light field camera can capture light information from various directions within a scene, allowing for the reconstruction of the scene. The light field image inherently contains the depth information of the scene, and depth estimations of light field images have become a popular research topic. This paper proposes a depth estimation network of light field images with occlusion awareness. Since light field images contain many views from different viewpoints, identifying the combinations that contribute the most to the depth estimation of the center view is critical to improving the depth estimation accuracy. Current methods typically rely on a fixed set of views, such as vertical, horizontal, and diagonal, which may not be optimal for all scenes. To address this limitation, we propose a novel approach that considers all available views during depth estimation while leveraging an attention mechanism to assign weights to each view dynamically. By inputting all views into the network and employing the attention mechanism, we enable the model to adaptively determine the most informative views for each scene, thus achieving more accurate depth estimation. Furthermore, we introduce a multi-scale feature fusion strategy that amalgamates contextual information and expands the receptive field to enhance the network's performance in handling challenging scenarios, such as textureless and occluded regions.
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