Three-dimensional reconstruction for highly reflective diffuse object based on online measurement

Li, Minghang; Cao, Yiping; Wu, Haitao

doi:10.1016/j.optcom.2023.129276

Cited by 6 publications

(5 citation statements)

References 36 publications

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“…This mechanism allows the model to focus on more critical global and local information, enhancing the handling of global structure and local details in threedimensional shapes and improving sensitivity to key features. Different from the approach of projecting from multiple views to obtain two-dimensional projections, Shi et al [17] first project the three-dimensional shape onto the outer surface of a corresponding cylinder. They then unfold the lateral surface of the cylinder to obtain a single two-dimensional panoramic view.…”

Section: Methods Based On Multiple Viewsmentioning

confidence: 99%

Classification of Typical Static Objects in Road Scenes Based on LO-Net

Li,

Wu,

Liu

et al. 2024

Remote Sensing

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Mobile LiDAR technology is a powerful tool that accurately captures spatial information about typical static objects in road scenes. However, the precise extraction and classification of these objects pose persistent technical challenges. In this paper, we employ a deep learning approach to tackle the point cloud classification problem. Despite the popularity of the PointNet++ network for direct point cloud processing, it encounters issues related to insufficient feature learning and low accuracy. To address these limitations, we introduce a novel layer-wise optimization network, LO-Net. Initially, LO-Net utilizes the set abstraction module from PointNet++ to extract initial local features. It further enhances these features through the edge convolution capabilities of GraphConv and optimizes them using the “Unite_module” for semantic enhancement. Finally, it employs a point cloud spatial pyramid joint pooling module, developed by the authors, for the multiscale pooling of final low-level local features. Combining three layers of local features, LO-Net sends them to the fully connected layer for accurate point cloud classification. Considering real-world scenarios, road scene data often consist of incomplete point cloud data due to factors such as occlusion. In contrast, models in public datasets are typically more complete but may not accurately reflect real-world conditions. To bridge this gap, we transformed road point cloud data collected by mobile LiDAR into a dataset suitable for network training. This dataset encompasses nine common road scene features; hence, we named it the Road9 dataset and conducted classification research based on this dataset. The experimental analysis demonstrates that the proposed algorithm model yielded favorable results on the public datasets ModelNet40, ModelNet10, and the Sydney Urban Objects Dataset, achieving accuracies of 91.2%, 94.2%, and 79.5%, respectively. On the custom road scene dataset, Road9, the algorithm model proposed in this paper demonstrated outstanding classification performance, achieving a classification accuracy of 98.5%.

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Section: Methods Based On Multiple Viewsmentioning

confidence: 99%

Classification of Typical Static Objects in Road Scenes Based on LO-Net

Li,

Wu,

Liu

et al. 2024

Remote Sensing

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“…20 Minghang Li et al proposed a 3D reconstruction method for highly reflective diffuse object. 21 Sieno et al opened the way for optical imaging using SiPM reflective mode source detector arrays. 22 Mimura obtained the first 3D tissue oxygen saturation map of the human thyroid gland using reflective configuration.…”

Section: Introductionmentioning

confidence: 99%

“…Minghang Li et al. proposed a 3D reconstruction method for highly reflective diffuse object 21 . Sieno et al.…”

Section: Introductionmentioning

confidence: 99%

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Reflective optical imaging for scattering medium using chaotic laser

Feng,

Yang,

et al. 2024

J. Biomed. Opt.

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Optical imaging is a non-invasive imaging technology that utilizes near-infrared light, allows for the image reconstruction of optical properties like diffuse and absorption coefficients within the tissue. A recent trend is to use signal processing techniques or new light sources and expanding its application.Aim: We aim to develop the reflective optical imaging using the chaotic correlation technology with chaotic laser and optimize the quality and spatial resolution of reflective optical imaging.Approach: Scattering medium was measured using reflective configuration in different inhomogeneous regions to evaluate the performance of the imaging system. The accuracy of the recovered optical properties was investigated. The reconstruction errors of absorption coefficients and geometric centers were analyzed, and the feature metrics of the reconstructed images were evaluated. Results:We showed how chaotic correlation technology can be utilized for information extraction and image reconstruction. This means that a higher signal-tonoise ratio and image reconstruction of inhomogeneous phantoms under different scenarios successfully were achieved.Conclusions: This work highlights that the peak values of correlation of chaotic exhibit smaller reconstruction error and better reconstruction performance in optical imaging compared with reflective optical imaging with the continuous wave laser.

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“…Although these methods are feasible, they rely on a priori values to determine the threshold range, and the threshold range can influence the measurement accuracy. Li et al proposed a post-processing-based approach to reconstruct three-dimensional shapes [25]. They obtained a complete reconstruction of the object by projecting the grating fringe patterns which do not overlap onto the surface of the measured object, ignored part of the reflective region, processed only the non-reflective region, and then performed a 3D point cloud matching after the rough reconstruction of the object contour by the moiré profilometry.…”

Section: Introductionmentioning

confidence: 99%

The Flatness Error Evaluation of Metal Workpieces Based on Line Laser Scanning Digital Imaging Technology

Mao,

Zhang,

Guo

et al. 2023

Photonics

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With the development of intelligent manufacturing, the production and assembly accuracy of components in factories is increasing in line with growing demand. However, the traditional manual quality inspection is inefficient, inaccurate, and costly. To this end, digital and optical imaging techniques are used to achieve intelligent quality inspection. However, during the reconstruction process, the high reflectivity of object materials affects the speed and accuracy of reconstruction results. To overcome these problems, this study investigated the three-dimensional (3D) digital imaging techniques based on line laser scanning. It advances a novel methodology for image segmentation, underpinned by deep learning algorithms, to augment the precision of the reconstruction results while simultaneously enhancing processing velocity. After the reconstruction phase, the research assesses flatness tolerance using point cloud registration technology. Finally, we constructed a measurement platform with a cost of less than CNY 100,000 (about USD 14,000) and obtained a measurement accuracy of 30 microns.

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Three-dimensional reconstruction for highly reflective diffuse object based on online measurement

Cited by 6 publications

References 36 publications

Classification of Typical Static Objects in Road Scenes Based on LO-Net

Classification of Typical Static Objects in Road Scenes Based on LO-Net

Reflective optical imaging for scattering medium using chaotic laser

The Flatness Error Evaluation of Metal Workpieces Based on Line Laser Scanning Digital Imaging Technology

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