3D Imaging Based on Depth Measurement Technologies

Ni, Chen; Zuo, Chao; Lam, Edmund Y.; Lee, Byoungho

doi:10.3390/s18113711

Cited by 25 publications

(6 citation statements)

References 207 publications

(395 reference statements)

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“…The 3D reconstruction technique is a process of obtaining the depth estimation and creating a 3D image of an object. 18 Leveraging the high temporal resolution of event sensors, depth information can be enhanced and the reconstruction error can also be reduced. For example, the fringe projection profilometry system, high-speed scanning, and accurate reconstruction are usually limited by the camera performance, especially by the response time.…”

Section: D Reconstructionmentioning

confidence: 99%

Computational neuromorphic imaging: principles and applications

Zhu,

Wang,

Liu

et al. 2024

Computational Optical Imaging and Artificial Intelligence in Biomedical Sciences

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The widespread presence and use of visual data highlight the fact that conventional frame-based electronic sensors may not be well-suited for specific situations. For instance, in many biomedical applications, there is a need to image dynamic specimens at high speeds, even though these objects occupy only a small fraction of the pixels within the entire field of view. Consequently, despite capturing them at a high frame rate, many resulting pixel values are uninformative and therefore discarded during subsequent computations. Neuromorphic imaging, which makes use of an event sensor that responds to changes in pixel intensities, is ideally suitable for detecting such fast-moving objects. In this work, we outline the principle of such detectors, demonstrate their use in a computational imaging setting, and discuss the computational algorithms to process such event data for a variety of applications.

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Section: D Reconstructionmentioning

confidence: 99%

Computational neuromorphic imaging: principles and applications

Zhu,

Wang,

Liu

et al. 2024

Computational Optical Imaging and Artificial Intelligence in Biomedical Sciences

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“…Radiance of light rays from every 3D point in an object is commonly represented by a 5D plenoptic function, F(x, y, z, θ, φ), parameterized by three spatial coordinates (x, y, z) and two angles (θ and φ) [18], [19]. When the radiance along the ray in free space is assumed to be a constant, it reduces to 4D [20].…”

Section: A Light Field Representation Theorymentioning

confidence: 99%

Computational Light Field Generation Using Deep Nonparametric Bayesian Learning

Meng

Sun

et al. 2019

IEEE Access

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In this paper, we present a deep nonparametric Bayesian method to synthesize a light field from a single image. Conventionally, light-field capture requires special optical architecture, and the gain in angular resolution often comes at the expense of a reduction in spatial resolution. Techniques for computationally generating the light field from a single image can be expanded further to a variety of applications, ranging from microscopy and materials analysis to vision-based robotic control and autonomous vehicles. We treat the light field as multiple sub-aperture views, and to compute the novel viewpoints, our model contains three major components. First, a convolutional neural network is used for predicting the depth probability map from the image. Second, a multi-scale feature dictionary is constructed within a multi-layer dictionary learning network. Third, the novel views are synthesized taking into account both the probabilistic depth map and the multi-scale feature dictionary. The experiments show that our method outperforms several state-of-the-art novel view synthesis methods in delivering good image resolution. INDEX TERMS Image reconstruction, convolutional neural network, deep learning, nonparametric Bayesian, light field imaging.

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“…As is known to all that depth estimation is one of the most important research contents in 3D reconstruction [9]. At present, there are two main ways to estimate depth information by using light field data.…”

Section: Introductionmentioning

confidence: 99%

Three-Dimensional Reconstruction of Light Field Based on Phase Similarity

Feng

Gao

et al. 2021

Sensors

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Light field imaging plays an increasingly important role in the field of three-dimensional (3D) reconstruction because of its ability to quickly obtain four-dimensional information (angle and space) of the scene. In this paper, a 3D reconstruction method of light field based on phase similarity is proposed to increase the accuracy of depth estimation and the scope of applicability of epipolar plane image (EPI). The calibration method of the light field camera was used to obtain the relationship between disparity and depth, and the projector calibration was removed to make the experimental procedure more flexible. Then, the disparity estimation algorithm based on phase similarity was designed to effectively improve the reliability and accuracy of disparity calculation, in which the phase information was used instead of the structure tensor, and the morphological processing method was used to denoise and optimize the disparity map. Finally, 3D reconstruction of the light field was realized by combining disparity information with the calibrated relationship. The experimental results showed that the reconstruction standard deviation of the two objects was 0.3179 mm and 0.3865 mm compared with the ground truth of the measured objects, respectively. Compared with the traditional EPI method, our method can not only make EPI perform well in a single scene or blurred texture situations but also maintain good reconstruction accuracy.

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3D Imaging Based on Depth Measurement Technologies

Cited by 25 publications

References 207 publications

Computational neuromorphic imaging: principles and applications

Computational neuromorphic imaging: principles and applications

Computational Light Field Generation Using Deep Nonparametric Bayesian Learning

Three-Dimensional Reconstruction of Light Field Based on Phase Similarity

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