基于事件信息与深度学习的高动态范围三维重建  

High Dynamic Range 3D Recontruction Based on Event Information and Deep Learning

作　　者：王杰 魏振东 王启江 张启灿[1] 王亚军 WANG Jie;WEI Zhendong;WANG Qijiang;ZHANG Qican;WANG Yajun(College of Electronics and Information Engineering,Sichuan University,Chengdu 610065,China)

机构地区：[1]四川大学电子信息学院,成都610065

出　　处：《数据采集与处理》2024年第2期337-347,共11页Journal of Data Acquisition and Processing

基　　金：四川省科技计划项目(2023NSFSC0496);国家自然科学基金(62075143)。

摘　　要：采用光学三维成像技术测量金属零件、黑色物体以及半透明物体等高动态范围(High dynamic range,HDR)表面的三维轮廓是一个极具挑战性的问题。目前,传统方法对存在较低反射以及半透明区域的场景进行重建还有一定的局限性,半透明物体的内部反射噪声很难消除。现有基于深度学习的方法通常使用相对较强的激光强度,这可能会损坏样品,同时会出现采集图像过曝现象,需要对激光强度进行繁琐的调整。针对这些问题,本文提出基于事件信息和深度学习算法的高动态场景三维测量方法。事件相机通过异步记录单个像素的亮度变化,无需等待全局曝光时间,具有高动态响应范围,能够充分采集到HDR场景的激光条纹反射信息。引入深度卷积神经网络(Deep convolutional neural network,DCNN)来消除半透明物体的内部噪声以及金属物体高反光的过曝影响,同时增强弱激光条纹图像质量。实验结果表明,本文方法能够应用低功率线激光扫描成功实现HDR场景的高质量三维重建。Three-dimentional(3D)measurement of high dynamic range(HDR)surfaces using optical 3D imaging technology,such as metal parts,black objects,and translucent objects,remains a challenging problem.Currently,traditional methods have limitations in reconstructing HDR scenes with low reflection and translucent areas,as well as difficulty in eliminating internal reflection noise of translucent objects.Existing deep learning-based methods typically use strong laser intensification,which can potentially damage the sample and result in overexposure of the acquired image,necessitating tedious adjustments to the laser intensity.To address these issues,this paper proposes a 3D measurement method for HDR scenes utilizing an event camera and the deep learning algorithm.By asynchronously recording the brightness changes of individual pixels,the event camera is with a high dynamic range response,and thus has the ability to fully capture the laser fringe of HDR scenes.In addition,we introduce a deep convolutional neural network(DCNN)to eliminate the noises caused by the reflections inside transparent objects and overexposure area of high reflection from metallic objects,while enhancing the weak laser stripes on the surface.Experimental results demonstrate that the proposed method can successfully achieve high-quality 3D reconstruction of HDR scenes utilizing low-power line laser scanning.

关 键 词：光学三维成像 事件相机 高动态范围 深度卷积神经网络 

分 类 号：TP391[自动化与计算机技术—计算机应用技术]