Abstract
Existing scanning laser three-dimensional (3D) imaging technology has slow measurement speed. In addition, the measurement accuracy of non-scanning laser 3D imaging technology based on area array detectors is limited by the resolution and response frequency of area array detectors. As a result, applications of laser 3D imaging technology are limited. This paper completed simulations and experiments of a non-scanning 3D imaging system with a single-pixel detector. The single-pixel detector can be used to achieve 3D imaging of a target by compressed sensing to overcome the shortcomings of the existing laser 3D imaging technology. First, the effects of different sampling rates, sparse transform bases, measurement matrices, and reconstruction algorithms on the measurement results were compared through simulation experiments. Second, a non-scanning 3D imaging experimental platform was designed and constructed. Finally, an experiment was performed to compare the effects of different sampling rates and reconstruction algorithms on the reconstruction effect of 3D imaging to obtain a 3D image with a resolution of 8 × 8. The simulation results show that the reconstruction effect of the Hadamard measurement matrix and the minimum total variation reconstruction algorithm performed well.
Highlights
At present, using three-dimensional (3D) imaging technology to obtain 3D information about the surrounding environment is important in many application fields, in the fields of autonomous driving, 3D printing, machine vision, and virtual reality [1,2]
To study the effects of different sampling rates, sparse transform bases, measurement matrices, and reconstruction algorithms on the effect of 3D imaging reconstruction in compressed sensing, simulation experiments were performed in the MATLAB software environment
Because the DMD micro-mirror can only represent 1 or 0, we select as the measurement matrix minimum total variation (TV) [10,20]
Summary
At present, using three-dimensional (3D) imaging technology to obtain 3D information about the surrounding environment is important in many application fields, in the fields of autonomous driving, 3D printing, machine vision, and virtual reality [1,2]. It was proven that for sparse or sparsely expressed signals, the original signal can be reconstructed accurately with a high probability using a small number of measurement times This resolves the contradiction between measurement resolution and measurement efficiency in traditional 3D imaging signal sampling and. University developed a single-pixel camera using the In theory compressed sensing; Takhar first makes. Rochester a single-pixel camera using the theory of compressed sensing; Takhar first applied compressed sensing used a photon-counting achieve 3D sensing imagingused [12] using a pulsed laser to imaging systems [11].method. 128, achieving an accuracy of mm within to realize 3D compressed sensing imaging [14] and accurately reconstructed a target scene witha distance of m. Paper, aasingle-pixel single-pixelnon-scanning non-scanning3D imaging system was designed, the system investigated in terms of theories, simulations, and experiments.
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