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通过极几何学和轴向注意力机制从光场进行深度估计

Lei Han, Zhan Shi, Xing Wang

|August 13, 2025

概括

本研究介绍了EGAA模型用于光场深度估计,利用极平面图像 (EPI) 几何和轴心注意力. 该模型有效地从EPI中提取深度线索,以改进3D重建和自主系统.

科学领域:

计算机视觉计算机视觉
机器学习机器学习

背景情况:

光场深度估计对于3D环境和自动驾驶至关重要.
脊柱平面图像 (EPI) 包含重要的深度线索,通常通过它们的线性纹理进行分析.

研究的目的:

通过分析EPI几何学和轴心注意力机制,开发一种用于光场深度估计的新型模型.
在复杂的光场数据中提高深度估计的准确性和效率.

主要方法:

提出了EGAA (EPI几何和轴向注意) 模型.
开发了一种具有多方向图像体积处理和双轴注意力块 (水平和垂直) 的编码器.
设计了一个解码器,堆叠着沙钟形的卷积神经网络块和跳过连接.

主要成果:

EGAA模型有效地利用EPI几何学和轴向注意力进行深度估计.
对比和消去实验在合成和真实光场数据集上显示出优越的定量和质量性能.

结论:

EGAA模型在光场深度估计方面取得了重大进展.
集成的EPI几何与轴注意力提供了一个强大的方法,深度暗示器提取.

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Depth Perception and Spatial Vision

Depth Perception and Spatial Vision

Depth perception is the ability to perceive objects three-dimensionally. It relies on two types of cues: binocular and monocular. Binocular cues depend on the combination of images from both eyes and how the eyes work together. Since the eyes are in slightly different positions, each eye captures a slightly different image. This disparity between images, known as binocular disparity, helps the brain interpret depth. When the brain compares these images, it determines the distance to an object.