High Dynamic Range 3D Recontruction Based on Event Information and Deep Learning
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.
optical 3D imagingevent camerahigh dynamic rangedeep convolutional neural network
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