Abstract

The application of artificial intelligence and deep learning in the fields of wireless communication, image and speech recognition, and 3D reconstruction has successfully solved some difficult modeling problems. This paper focuses on the high‐precision 3D reconstruction of the motion‐blurred cooperative markers, including the Chinese character coded targets (CCTs) and the noncoded circular markers. A simulation‐based motion‐blurred image generation model is constructed to provide sufficient samples for training the convolutional neural network to identify and match the motion‐blurred CCTs on the moving object. The blurred noncoded marker matching is performed through homography. The 3D reconstruction of the markers is realized via the optimization of the spatial moving path within the exposure period. The midpoint of the moving path of the markers is taken as the final reconstruction result. The experimental results show that the 3D reconstruction accuracy of the markers with a certain motion blur effect is about 0.08 mm.

Highlights

  • Machine vision has been widely used in object positioning, pose detection, motion tracking, and 3D shape reconstruction [1,2,3,4,5]

  • In [12], the sharp boundary template was extracted from the downsampled image of the blurred image, and the blurred and the predicted value images were used to calculate the blur kernel

  • Taking multiview images with motion blur effect as input, we propose an approach to 3D reconstruction of corporative markers, including both coded and noncoded ones

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Summary

Introduction

Machine vision has been widely used in object positioning, pose detection, motion tracking, and 3D shape reconstruction [1,2,3,4,5]. The above-mentioned methods can restore the motion-blurred images to some extent, the algorithms still need to make assumptions about the motion of the target or the camera These assumptions implied the uniformity of the blur, which is usually not the case in practice. In [21], a segmentation-based symmetrical stereo vision matching method was proposed to address the high matching error rates of the images with motion blur This method effectively reduced the false matching rates, it was only suitable for images with slight local blurring. In [22], a motion model based on the affine transformation principle was established This method estimated the motion blur parameters of each subregion, but it was only suitable for problems tolerating relative low accuracy.

The Marker Targets
Recognition of the Motion-Blurred CCTs
The 3D Reconstruction Based on Image Difference Minimization
Experiments
Conclusions
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